[공모전 수상작 리뷰] Reactjs+Nodejs+python+scikit-learn{ PCA(주성분 분석), VAR(다변량시계열분석)}으로 공연 예매 추이 시나리오 별 예측하는 서비스 만들어보기 - 데이터 분석 편(3)
확정 모델(m9)로 직접 공연 예매 건수 예측해보기
기본적인 피처 설명
기간: 2019.01.01 ~ 2021.08.31
ott_user_count: OTT앱 일 별 사용자 수,
ott_usage_time: OTT앱 일 별 사용시간,
delivery_user_count: 배달앱 일 별 사용자 수,
delivery_usage_time: 배달앱 일 별 사용시간,
used_user_count: 중고거래앱 일 별 사용자 수,
used_usage_time: 중고거래앱 일 별 사용시간,
meeting_user_count: 화상회의앱 일 별 사용자 수,
meeting_usage_time: 화상회의앱 일 별 사용시간,
corona_count: 일 별 코로나 확진자 수,
subway_count: 일 별 지하철 이용자 수,
KOSPI_index: 일 별 코스피 지수,
KOSPI_trading: 일 별 코스피 시장 거래량,
KOSDAQ_index: 일 별 코스닥 지수,
KOSDAQ_trading: 일 별 코스닥 시장 거래량,
coin_trading: 일 별 가상화폐(비트코인+이더리움)거래량 평균,
coin_variance: 전 일 대비 일 별 가상화폐(비트코인+이더리움)등락률 평균,
#필요 라이브러리 로드
import numpy as np
import pandas as pd
import seaborn as sns
from statsmodels.stats.outliers_influence import variance_inflation_factor
import matplotlib.pyplot as plt
import matplotlib.font_manager as fm
import matplotlib
matplotlib.font_manager._rebuild()
from sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler,Normalizer
from sklearn.preprocessing import OneHotEncoder, LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.decomposition import PCA
from statsmodels.tsa.api import VAR
from statsmodels.tsa.stattools import adfuller
sns.set(style='whitegrid')
pd.set_option('display.max_rows',500)
font_path = r'경로\\NanumFontSetup_TTF_GOTHIC.NanumGothic.ttf'
fontprop = fm.FontProperties(fname=font_path, size=18)데이터 로드 및 기초 전처리
#데이터 로드 및 기초 전처리
df = pd.read_csv("저장링크\\201901_202108_종합통계_시계열분석용.csv")
df.drop('Unnamed: 0', axis=1, inplace=True)
df['corona_count'].fillna(0,inplace=True)
df['coin_trading'] = df['bitcoin_trading']+df['ethereum_trading']
df['coin_variance'] = (df['bitcoin_variance']+df['ethereum_variance'])/2
df.drop(['bitcoin_trading','ethereum_trading',
'bitcoin_variance','ethereum_variance'],axis=1,inplace=True)
df.index = df['date']
df_date = df['date']
df.drop(['date'],axis=1, inplace=True)타겟,피처 분리 및 스탠다드스케일링 수행
X = df.iloc[:,1:]
y = df.iloc[:,0]#StandardScaler 객체 생성
scaler = StandardScaler()
#StandardScaler로 데이터 셋 변환, fit()과 transform()호출
scaler.fit(X)
X_scaled = scaler.transform(X)
X_scaled = pd.DataFrame(data=X_scaled, columns=X.columns)
X_scaled.index = df_date
X_scaled| ott_user_count | ott_usage_time | delivery_user_count | delivery_usage_time | used_user_count | used_usage_time | meeting_user_count | meeting_usage_time | corona_count | subway_count | KOSPI_index | KOSPI_trading | KOSDAQ_index | KOSDAQ_trading | coin_trading | coin_variance | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| date | ||||||||||||||||
| 2019/01/01 | -1.301964 | -1.126337 | -1.138730 | -0.879145 | -1.332576 | -1.373703 | -1.053405 | -0.843484 | -0.607122 | -1.546825 | -0.857302 | -1.152997 | -0.801456 | -1.333287 | -0.760446 | 0.894595 |
| 2019/01/02 | -1.411287 | -1.360638 | -1.542213 | -1.360824 | -1.294828 | -1.363849 | -0.816423 | -0.804730 | -0.607122 | 0.758960 | -0.857302 | -1.152997 | -0.801456 | -1.333287 | -0.473632 | 1.209898 |
| 2019/01/03 | -1.512255 | -1.380048 | -1.536694 | -1.377820 | -1.179579 | -1.346215 | -0.813926 | -0.802878 | -0.607122 | 0.908731 | -0.892203 | -0.903009 | -0.886813 | -1.155502 | -0.653263 | -0.830298 |
| 2019/01/04 | -1.343318 | -1.318085 | -1.434606 | -1.294248 | -1.309262 | -1.362754 | -0.819952 | -0.804077 | -0.607122 | 1.091537 | -0.856767 | -0.947702 | -0.835184 | -1.310455 | -0.547525 | 0.442506 |
| 2019/01/05 | -1.010367 | -0.963209 | -1.174507 | -0.999621 | -1.319738 | -1.330514 | -1.046767 | -0.836480 | -0.607122 | -0.153104 | -0.856767 | -0.947702 | -0.835184 | -1.310455 | -0.557694 | -0.100001 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2021/08/27 | 1.656060 | 1.539082 | 2.271937 | 2.151560 | 1.174091 | 0.778091 | 1.484776 | 1.743173 | 3.594865 | 1.533985 | 1.549169 | -0.705340 | 1.646165 | -0.178085 | -1.055345 | 1.133390 |
| 2021/08/28 | 1.780755 | 1.632663 | 2.442208 | 2.699594 | 1.484143 | 1.128198 | -0.075720 | 0.000089 | 3.187087 | -0.002058 | 1.549169 | -0.705340 | 1.646165 | -0.178085 | -1.100312 | -0.239105 |
| 2021/08/29 | 1.692069 | 2.029458 | 2.325626 | 2.782625 | 1.308575 | 1.197393 | 0.060223 | 0.137999 | 2.877739 | -0.921063 | 1.549169 | -0.705340 | 1.646165 | -0.178085 | -1.079082 | -0.199692 |
| 2021/08/30 | 1.312763 | 1.266002 | 1.502044 | 1.133586 | 1.316112 | 0.847673 | 1.702709 | 1.957982 | 2.608230 | 1.558606 | 1.571202 | -0.509248 | 1.703738 | -0.361816 | -1.041119 | -0.505722 |
| 2021/08/31 | 1.434144 | 1.282830 | 1.685858 | 1.606643 | 1.279019 | 0.879251 | 1.712669 | 1.997506 | 4.138569 | 1.394636 | 1.689138 | -0.386843 | 1.748593 | -0.262413 | -0.977654 | 0.676665 |
974 rows × 16 columns
변수 2개로 PCA 수행
pca = PCA(n_components=2)
printcipalComponents = pca.fit_transform(X_scaled)
principalDf = pd.DataFrame(data=printcipalComponents, columns = ['p1','p2'])print(principalDf.head())
print(pca.explained_variance_ratio_)
print(sum(pca.explained_variance_ratio_)) p1 p2
0 -3.472938 0.264457
1 -4.064765 -1.172083
2 -4.014694 -1.128189
3 -3.982567 -1.288346
4 -3.529418 -0.322702
[0.63059992 0.10080491]
0.7314048320656544principalDf.index = df_date
principalDf| p1 | p2 | |
|---|---|---|
| date | ||
| 2019/01/01 | -3.472938 | 0.264457 |
| 2019/01/02 | -4.064765 | -1.172083 |
| 2019/01/03 | -4.014694 | -1.128189 |
| 2019/01/04 | -3.982567 | -1.288346 |
| 2019/01/05 | -3.529418 | -0.322702 |
| ... | ... | ... |
| 2021/08/27 | 5.117077 | -3.194164 |
| 2021/08/28 | 4.866254 | -1.415963 |
| 2021/08/29 | 5.011647 | -0.716045 |
| 2021/08/30 | 4.346513 | -3.065882 |
| 2021/08/31 | 5.077023 | -3.389693 |
974 rows × 2 columns
df=principalDfprincipalDf.index = df_date
df = pd.merge(y, principalDf,left_index=True, right_index=True,how='inner')
df| ticketing_count | p1 | p2 | |
|---|---|---|---|
| date | |||
| 2019/01/01 | 7401 | -3.472938 | 0.264457 |
| 2019/01/02 | 5069 | -4.064765 | -1.172083 |
| 2019/01/03 | 6498 | -4.014694 | -1.128189 |
| 2019/01/04 | 7088 | -3.982567 | -1.288346 |
| 2019/01/05 | 18755 | -3.529418 | -0.322702 |
| ... | ... | ... | ... |
| 2021/08/27 | 19582 | 5.117077 | -3.194164 |
| 2021/08/28 | 45456 | 4.866254 | -1.415963 |
| 2021/08/29 | 31871 | 5.011647 | -0.716045 |
| 2021/08/30 | 3652 | 4.346513 | -3.065882 |
| 2021/08/31 | 8582 | 5.077023 | -3.389693 |
974 rows × 3 columns
정상성확인
for i in df.columns:
adfuller_test = adfuller(df[i],autolag='AIC')
print(i)
print("ADF test statistic: {}".format(adfuller_test[0]))
print("p-value: {}".format(adfuller_test[1]))
df_diff = df.diff().dropna()
df_diff.plot(figsize=(20,20))
print(df_diff)
for i in df.columns:
adfuller_test = adfuller(df_diff[i],autolag='AIC')
print(i)
print("ADF test statistic: {}".format(adfuller_test[0]))
print("p-value: {}".format(adfuller_test[1]))
train = df_diff.iloc[:-30,:]
test = df_diff.iloc[-30:,:]
print(train, test)
forecasting_model = VAR(train)
results_aic = []
for p in range(1,30):
results = forecasting_model.fit(p)
results_aic.append(results.aic)
sns.set()
plt.plot(list(np.arange(1,30,1)), results_aic)
plt.xlabel("Order")
plt.ylabel("AIC")
plt.show()
for i in results_aic:
print(i)
print("최적순서")
print(np.argsort(results_aic)[0])
print(results_aic[np.argsort(results_aic)[0]])
results = forecasting_model.fit(np.argsort(results_aic)[0])
results.summary()
laaged_values = train.values
forecast = pd.DataFrame(results.forecast(y= laaged_values, steps=30), index = test.index,\
columns=df.columns)
for i in df.columns:
forecast[f'{i}_forecasted']= df[i].iloc[-30-1]+forecast[i].cumsum()
print(forecast)
test = df.iloc[-30:,:1]
for i in test.columns:
test[f'{i}_forecasted'] = forecast[f'{i}_forecasted']
test.plot(figsize=(20,20))
mse = mean_squared_error(test['ticketing_count'], test['ticketing_count_forecasted'])
rmse = np.sqrt(mse)
print(f'MSE: {mse}')
print(f'RMSE: {rmse}')
print('Variance score: {0:.3f}'.format(r2_score(test['ticketing_count'],
test['ticketing_count_forecasted'])))
# for i in range(3):
# test = df.iloc[-30:,i:i+1]
# for i in test.columns:
# test[f'{i}_forecasted'] = forecast[f'{i}_forecasted']
# test.plot(figsize=(20,20))ticketing_count
ADF test statistic: -2.099553733500803
p-value: 0.24469408536639126
p1
ADF test statistic: -0.21267008715649313
p-value: 0.9369944290578626
p2
ADF test statistic: -0.9660338259001354
p-value: 0.7654502757577035
ticketing_count p1 p2
date
2019/01/02 -2332.0 -0.591827 -1.436540
2019/01/03 1429.0 0.050071 0.043894
2019/01/04 590.0 0.032127 -0.160157
2019/01/05 11667.0 0.453149 0.965645
2019/01/06 -5564.0 0.215044 0.535168
... ... ... ...
2021/08/27 3970.0 0.335959 0.163582
2021/08/28 25874.0 -0.250823 1.778201
2021/08/29 -13585.0 0.145392 0.699918
2021/08/30 -28219.0 -0.665134 -2.349837
2021/08/31 4930.0 0.730510 -0.323811
[973 rows x 3 columns]
ticketing_count
ADF test statistic: -8.90366524755091
p-value: 1.1532103667357817e-14
p1
ADF test statistic: -7.316333641273258
p-value: 1.2265309593054393e-10
p2
ADF test statistic: -8.654863167499396
p-value: 5.0006128672818535e-14
ticketing_count p1 p2
date
2019/01/02 -2332.0 -0.591827 -1.436540
2019/01/03 1429.0 0.050071 0.043894
2019/01/04 590.0 0.032127 -0.160157
2019/01/05 11667.0 0.453149 0.965645
2019/01/06 -5564.0 0.215044 0.535168
... ... ... ...
2021/07/28 7195.0 0.271976 -0.397341
2021/07/29 -1568.0 0.121974 0.071939
2021/07/30 1240.0 -0.323097 -0.009642
2021/07/31 11692.0 0.806326 1.693124
2021/08/01 -1419.0 -0.083921 0.568040
[943 rows x 3 columns] ticketing_count p1 p2
date
2021/08/02 -23309.0 -1.197897 -1.823121
2021/08/03 6021.0 0.382762 -0.307690
2021/08/04 5184.0 0.224371 -0.159779
2021/08/05 -1820.0 0.213700 0.045172
2021/08/06 2199.0 0.210503 0.053557
2021/08/07 15793.0 0.433313 1.324466
2021/08/08 -6053.0 0.227528 0.915187
2021/08/09 -22049.0 -1.476812 -2.419552
2021/08/10 4498.0 0.864029 -0.313854
2021/08/11 5951.0 -0.361891 0.011769
2021/08/12 -1827.0 -0.178059 -0.032996
2021/08/13 5810.0 0.349272 0.207221
2021/08/14 14591.0 0.313612 1.626783
2021/08/15 -7036.0 -0.030712 0.621387
2021/08/16 -18277.0 -0.492023 -0.229886
2021/08/17 1743.0 -0.320392 -2.102970
2021/08/18 10810.0 0.231022 -0.418104
2021/08/19 -3599.0 0.186924 0.148053
2021/08/20 1624.0 0.140931 0.255090
2021/08/21 24481.0 0.576043 2.264528
2021/08/22 -9835.0 -0.445505 0.305457
2021/08/23 -28924.0 -0.563361 -2.491265
2021/08/24 9165.0 0.635171 -0.388304
2021/08/25 9759.0 -0.326547 -0.162770
2021/08/26 -7134.0 0.101249 0.099862
2021/08/27 3970.0 0.335959 0.163582
2021/08/28 25874.0 -0.250823 1.778201
2021/08/29 -13585.0 0.145392 0.699918
2021/08/30 -28219.0 -0.665134 -2.349837
2021/08/31 4930.0 0.730510 -0.323811
C:\Users\USER\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:162: ValueWarning: No frequency information was provided, so inferred frequency D will be used.
% freq, ValueWarning)
15.874141457581395
15.5563271912253
15.426246152939317
15.318750926870058
14.483015895965174
13.814739596824277
13.673927483926283
13.665937798337964
13.663878431792096
13.676651939760099
13.692038994826223
13.677564677917928
13.52434197981419
13.490367614527221
13.499927178777222
13.512585794850738
13.529358365994831
13.542355918442388
13.539247800974483
13.496007805199893
13.470538807423434
13.488222723432752
13.499948384661002
13.511730951232966
13.521836126222121
13.538180085837523
13.521278819452538
13.51962317725291
13.531074509808017
최적순서
20
13.470538807423434
ticketing_count p1 p2 ticketing_count_forecasted \
date
2021/08/02 -21642.828430 -1.033055 -1.920281 6203.171570
2021/08/03 4108.724575 0.139701 -0.231206 10311.896145
2021/08/04 6880.360833 0.272476 -0.069083 17192.256978
2021/08/05 -2316.669341 0.105379 -0.046469 14875.587637
2021/08/06 3700.776692 0.056802 0.183212 18576.364329
2021/08/07 15584.686419 0.523810 1.435338 34161.050747
2021/08/08 -7120.885435 0.155960 0.662741 27040.165312
2021/08/09 -21598.560364 -1.056706 -1.925830 5441.604948
2021/08/10 6374.681052 0.093848 -0.235443 11816.286001
2021/08/11 4835.465688 0.167524 0.017505 16651.751688
2021/08/12 -1061.610712 0.108480 -0.038615 15590.140976
2021/08/13 3194.224752 0.057892 0.030914 18784.365728
2021/08/14 15751.719886 0.590367 1.493483 34536.085615
2021/08/15 -8752.597204 0.148820 0.582362 25783.488410
2021/08/16 -19415.434895 -0.958010 -1.771422 6368.053516
2021/08/17 4613.948331 0.059555 -0.313527 10982.001847
2021/08/18 6870.373239 0.149024 0.107671 17852.375086
2021/08/19 -2616.504104 0.089648 -0.079262 15235.870982
2021/08/20 3316.261836 0.029082 -0.012249 18552.132817
2021/08/21 15465.099080 0.534741 1.439276 34017.231897
2021/08/22 -8338.881623 0.123938 0.542337 25678.350274
2021/08/23 -19732.722387 -0.867167 -1.697932 5945.627887
2021/08/24 5293.242265 -0.005365 -0.337871 11238.870152
2021/08/25 5897.342288 0.144592 0.139256 17136.212440
2021/08/26 -2098.080567 0.064798 -0.095532 15038.131874
2021/08/27 3423.032627 0.060817 0.020882 18461.164501
2021/08/28 15996.419583 0.515684 1.370608 34457.584084
2021/08/29 -8996.299123 0.147419 0.538153 25461.284961
2021/08/30 -19438.048266 -0.827387 -1.619591 6023.236695
2021/08/31 5025.855059 -0.024818 -0.358857 11049.091753
p1_forecasted p2_forecasted
date
2021/08/02 4.050834 -2.306268
2021/08/03 4.190535 -2.537474
2021/08/04 4.463011 -2.606557
2021/08/05 4.568389 -2.653026
2021/08/06 4.625191 -2.469813
2021/08/07 5.149000 -1.034475
2021/08/08 5.304960 -0.371734
2021/08/09 4.248254 -2.297564
2021/08/10 4.342102 -2.533007
2021/08/11 4.509626 -2.515502
2021/08/12 4.618106 -2.554117
2021/08/13 4.675998 -2.523203
2021/08/14 5.266365 -1.029720
2021/08/15 5.415185 -0.447358
2021/08/16 4.457175 -2.218780
2021/08/17 4.516731 -2.532307
2021/08/18 4.665754 -2.424636
2021/08/19 4.755402 -2.503899
2021/08/20 4.784485 -2.516147
2021/08/21 5.319226 -1.076871
2021/08/22 5.443164 -0.534534
2021/08/23 4.575996 -2.232466
2021/08/24 4.570632 -2.570337
2021/08/25 4.715224 -2.431080
2021/08/26 4.780021 -2.526612
2021/08/27 4.840838 -2.505730
2021/08/28 5.356523 -1.135123
2021/08/29 5.503941 -0.596969
2021/08/30 4.676555 -2.216561
2021/08/31 4.651737 -2.575418
MSE: 13358737.141434086
RMSE: 3654.9606210510788
Variance score: 0.890
다변량시계열분석을 한번에 하는 함수 선언
#변수로 전처리 및 PCA가 완료된 데이터프레임(df) 및 예측 및 평가를 원하는 일 수(day)를 입력
def get_result(df, day):
print("차분 전 정상성 평가")
for i in df.columns:
adfuller_test = adfuller(df[i],autolag='AIC')
print(i)
print("ADF test statistic: {}".format(adfuller_test[0]))
print("p-value: {}".format(adfuller_test[1]))
df_diff = df.diff().dropna()
print("차분 플롯")
df_diff.plot(figsize=(20,20))
print("차분")
print(df_diff)
print("차분 후 정상성 평가")
for i in df.columns:
adfuller_test = adfuller(df_diff[i],autolag='AIC')
print(i)
print("ADF test statistic: {}".format(adfuller_test[0]))
print("p-value: {}".format(adfuller_test[1]))
print("학습, 테스트 데이터 분리")
train = df_diff.iloc[:-day,:]
test = df_diff.iloc[-day:,:]
print(train, test)
print("VAR예측모델 생성")
forecasting_model = VAR(train)
results_aic = []
for p in range(1,30):
results = forecasting_model.fit(p)
results_aic.append(results.aic)
print("AIC 확인")
sns.set()
plt.plot(list(np.arange(1,30,1)), results_aic)
plt.xlabel("Order")
plt.ylabel("AIC")
plt.show()
print(results_aic)
print("최적값 확인")
results = forecasting_model.fit(np.argsort(results_aic)[0])
print(results.summary())
print(f"입력한{day}일 차분 예측")
laaged_values = train.values
forecast = pd.DataFrame(results.forecast(y= laaged_values, steps=day), index = test.index,\
columns=df.columns)
print(f"입력한{day}일 차분을 더해서 원래값 예측")
for i in df.columns:
forecast[f'{i}_forecasted']= df[i].iloc[-day-1]+forecast[i].cumsum()
print(forecast)
test = df.iloc[-day:,:1]
for i in test.columns:
test[f'{i}_forecasted'] = forecast[f'{i}_forecasted']
test.plot(figsize=(20,20))
mse = mean_squared_error(test['ticketing_count'], test['ticketing_count_forecasted'])
rmse = np.sqrt(mse)
print(f'MSE: {mse}')
print(f'RMSE: {rmse}')
print('Variance score: {0:.3f}'.format(r2_score(test['ticketing_count'],
test['ticketing_count_forecasted'])))
for i in range(3):
test = df.iloc[-day:,i:i+1]
for i in test.columns:
test[f'{i}_forecasted'] = forecast[f'{i}_forecasted']
test.plot(figsize=(20,20))
test = df.iloc[-day:,:1]
for i in test.columns:
test[f'{i}_forecasted'] = forecast[f'{i}_forecasted']
return np.round(test),df_difftest,diff = get_result(df, 15)차분 전 정상성 평가
ticketing_count
ADF test statistic: -2.099553733500803
p-value: 0.24469408536639126
p1
ADF test statistic: -0.21267008715649313
p-value: 0.9369944290578626
p2
ADF test statistic: -0.9660338259001354
p-value: 0.7654502757577035
차분 플롯
차분
ticketing_count p1 p2
date
2019/01/02 -2332.0 -0.591827 -1.436540
2019/01/03 1429.0 0.050071 0.043894
2019/01/04 590.0 0.032127 -0.160157
2019/01/05 11667.0 0.453149 0.965645
2019/01/06 -5564.0 0.215044 0.535168
... ... ... ...
2021/08/27 3970.0 0.335959 0.163582
2021/08/28 25874.0 -0.250823 1.778201
2021/08/29 -13585.0 0.145392 0.699918
2021/08/30 -28219.0 -0.665134 -2.349837
2021/08/31 4930.0 0.730510 -0.323811
[973 rows x 3 columns]
차분 후 정상성 평가
ticketing_count
ADF test statistic: -8.90366524755091
p-value: 1.1532103667357817e-14
p1
ADF test statistic: -7.316333641273258
p-value: 1.2265309593054393e-10
p2
ADF test statistic: -8.654863167499396
p-value: 5.0006128672818535e-14
학습, 테스트 데이터 분리
ticketing_count p1 p2
date
2019/01/02 -2332.0 -0.591827 -1.436540
2019/01/03 1429.0 0.050071 0.043894
2019/01/04 590.0 0.032127 -0.160157
2019/01/05 11667.0 0.453149 0.965645
2019/01/06 -5564.0 0.215044 0.535168
... ... ... ...
2021/08/12 -1827.0 -0.178059 -0.032996
2021/08/13 5810.0 0.349272 0.207221
2021/08/14 14591.0 0.313612 1.626783
2021/08/15 -7036.0 -0.030712 0.621387
2021/08/16 -18277.0 -0.492023 -0.229886
[958 rows x 3 columns] ticketing_count p1 p2
date
2021/08/17 1743.0 -0.320392 -2.102970
2021/08/18 10810.0 0.231022 -0.418104
2021/08/19 -3599.0 0.186924 0.148053
2021/08/20 1624.0 0.140931 0.255090
2021/08/21 24481.0 0.576043 2.264528
2021/08/22 -9835.0 -0.445505 0.305457
2021/08/23 -28924.0 -0.563361 -2.491265
2021/08/24 9165.0 0.635171 -0.388304
2021/08/25 9759.0 -0.326547 -0.162770
2021/08/26 -7134.0 0.101249 0.099862
2021/08/27 3970.0 0.335959 0.163582
2021/08/28 25874.0 -0.250823 1.778201
2021/08/29 -13585.0 0.145392 0.699918
2021/08/30 -28219.0 -0.665134 -2.349837
2021/08/31 4930.0 0.730510 -0.323811
VAR예측모델 생성
C:\Users\USER\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:162: ValueWarning: No frequency information was provided, so inferred frequency D will be used.
% freq, ValueWarning)
AIC 확인
[15.8737022538001, 15.554278410271971, 15.42503726462058, 15.313255688920648, 14.476857357042636, 13.799670206938172, 13.661354687346684, 13.652744965087638, 13.65034394549885, 13.663066176963177, 13.676870884127531, 13.66162318423407, 13.507055202897341, 13.47244459716127, 13.482325799633918, 13.494281207808443, 13.510649001542241, 13.522535370334111, 13.519399218185791, 13.476976838091648, 13.453097385373527, 13.470259209040648, 13.48221580482198, 13.494567255165336, 13.505613450190017, 13.521063949836032, 13.504017247110996, 13.503720886774584, 13.514578922713437]
최적값 확인
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Tue, 07, Sep, 2021
Time: 20:59:59
--------------------------------------------------------------------
No. of Equations: 3.00000 BIC: 14.4220
Nobs: 938.000 HQIC: 13.8373
Log likelihood: -10130.6 FPE: 713208.
AIC: 13.4770 Det(Omega_mle): 590375.
--------------------------------------------------------------------
Results for equation ticketing_count
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 137.397069 216.604304 0.634 0.526
L1.ticketing_count -0.513584 0.034014 -15.099 0.000
L1.p1 -1759.508880 809.329167 -2.174 0.030
L1.p2 -2814.672509 491.647557 -5.725 0.000
L2.ticketing_count -0.593237 0.038117 -15.564 0.000
L2.p1 -1337.527594 837.057275 -1.598 0.110
L2.p2 -604.732473 523.399023 -1.155 0.248
L3.ticketing_count -0.399109 0.042916 -9.300 0.000
L3.p1 -1310.129759 853.557399 -1.535 0.125
L3.p2 -1047.185430 556.290605 -1.882 0.060
L4.ticketing_count -0.260825 0.044848 -5.816 0.000
L4.p1 -425.549570 871.647245 -0.488 0.625
L4.p2 -1215.857029 577.668676 -2.105 0.035
L5.ticketing_count -0.338953 0.045827 -7.396 0.000
L5.p1 -1259.837229 877.772932 -1.435 0.151
L5.p2 -400.778550 599.968983 -0.668 0.504
L6.ticketing_count -0.104848 0.047231 -2.220 0.026
L6.p1 -709.122596 891.947324 -0.795 0.427
L6.p2 -957.571826 616.884932 -1.552 0.121
L7.ticketing_count 0.163356 0.047392 3.447 0.001
L7.p1 -453.748264 893.053827 -0.508 0.611
L7.p2 -933.848854 628.666134 -1.485 0.137
L8.ticketing_count 0.019906 0.047132 0.422 0.673
L8.p1 -410.380715 902.292000 -0.455 0.649
L8.p2 338.790295 626.418795 0.541 0.589
L9.ticketing_count -0.043334 0.046903 -0.924 0.356
L9.p1 -489.041316 902.170843 -0.542 0.588
L9.p2 -347.532906 623.858243 -0.557 0.577
L10.ticketing_count -0.101521 0.046698 -2.174 0.030
L10.p1 -303.387984 902.826540 -0.336 0.737
L10.p2 -410.586762 625.347580 -0.657 0.511
L11.ticketing_count -0.174002 0.046706 -3.725 0.000
L11.p1 -459.367396 901.993573 -0.509 0.611
L11.p2 -92.840724 626.130881 -0.148 0.882
L12.ticketing_count -0.159532 0.047063 -3.390 0.001
L12.p1 306.233096 901.230007 0.340 0.734
L12.p2 -925.659501 624.161084 -1.483 0.138
L13.ticketing_count -0.244851 0.047358 -5.170 0.000
L13.p1 -81.061584 901.218637 -0.090 0.928
L13.p2 -99.412064 627.224878 -0.158 0.874
L14.ticketing_count 0.059139 0.047729 1.239 0.215
L14.p1 -529.121169 896.231262 -0.590 0.555
L14.p2 -203.885819 628.994392 -0.324 0.746
L15.ticketing_count -0.027799 0.047620 -0.584 0.559
L15.p1 -1061.660926 895.302216 -1.186 0.236
L15.p2 370.652086 615.832141 0.602 0.547
L16.ticketing_count -0.021627 0.046164 -0.468 0.639
L16.p1 485.528543 884.342606 0.549 0.583
L16.p2 -828.731277 597.190638 -1.388 0.165
L17.ticketing_count -0.068679 0.045335 -1.515 0.130
L17.p1 -1817.344001 872.715650 -2.082 0.037
L17.p2 -330.691981 581.849809 -0.568 0.570
L18.ticketing_count -0.134263 0.043241 -3.105 0.002
L18.p1 -330.376928 857.169463 -0.385 0.700
L18.p2 -577.773017 559.515509 -1.033 0.302
L19.ticketing_count -0.129775 0.038360 -3.383 0.001
L19.p1 -987.140587 844.228315 -1.169 0.242
L19.p2 -1069.787874 522.753937 -2.046 0.041
L20.ticketing_count -0.155562 0.033262 -4.677 0.000
L20.p1 -599.380228 815.244277 -0.735 0.462
L20.p2 -512.530482 501.928095 -1.021 0.307
======================================================================================
Results for equation p1
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.020551 0.009499 2.163 0.031
L1.ticketing_count 0.000000 0.000001 0.048 0.961
L1.p1 -0.299464 0.035493 -8.437 0.000
L1.p2 -0.121589 0.021561 -5.639 0.000
L2.ticketing_count -0.000005 0.000002 -2.787 0.005
L2.p1 -0.215902 0.036709 -5.881 0.000
L2.p2 -0.029040 0.022954 -1.265 0.206
L3.ticketing_count -0.000003 0.000002 -1.810 0.070
L3.p1 -0.229319 0.037433 -6.126 0.000
L3.p2 -0.051983 0.024396 -2.131 0.033
L4.ticketing_count -0.000005 0.000002 -2.637 0.008
L4.p1 -0.136179 0.038226 -3.562 0.000
L4.p2 -0.070959 0.025333 -2.801 0.005
L5.ticketing_count -0.000003 0.000002 -1.319 0.187
L5.p1 -0.210324 0.038494 -5.464 0.000
L5.p2 -0.020912 0.026311 -0.795 0.427
L6.ticketing_count -0.000005 0.000002 -2.224 0.026
L6.p1 -0.104482 0.039116 -2.671 0.008
L6.p2 -0.052080 0.027053 -1.925 0.054
L7.ticketing_count -0.000003 0.000002 -1.233 0.218
L7.p1 0.157207 0.039165 4.014 0.000
L7.p2 0.004342 0.027570 0.157 0.875
L8.ticketing_count -0.000003 0.000002 -1.264 0.206
L8.p1 -0.000355 0.039570 -0.009 0.993
L8.p2 -0.000437 0.027471 -0.016 0.987
L9.ticketing_count -0.000004 0.000002 -1.713 0.087
L9.p1 -0.055119 0.039564 -1.393 0.164
L9.p2 -0.019543 0.027359 -0.714 0.475
L10.ticketing_count -0.000000 0.000002 -0.135 0.893
L10.p1 0.022696 0.039593 0.573 0.566
L10.p2 -0.046427 0.027424 -1.693 0.090
L11.ticketing_count -0.000002 0.000002 -0.743 0.458
L11.p1 0.026180 0.039557 0.662 0.508
L11.p2 -0.024453 0.027459 -0.891 0.373
L12.ticketing_count -0.000001 0.000002 -0.582 0.561
L12.p1 0.039089 0.039523 0.989 0.323
L12.p2 -0.042649 0.027372 -1.558 0.119
L13.ticketing_count -0.000000 0.000002 -0.215 0.830
L13.p1 0.018826 0.039523 0.476 0.634
L13.p2 -0.049937 0.027507 -1.815 0.069
L14.ticketing_count -0.000001 0.000002 -0.326 0.745
L14.p1 0.110330 0.039304 2.807 0.005
L14.p2 0.018926 0.027584 0.686 0.493
L15.ticketing_count 0.000002 0.000002 0.882 0.378
L15.p1 -0.042184 0.039263 -1.074 0.283
L15.p2 -0.008542 0.027007 -0.316 0.752
L16.ticketing_count 0.000002 0.000002 0.927 0.354
L16.p1 -0.026036 0.038783 -0.671 0.502
L16.p2 -0.048500 0.026190 -1.852 0.064
L17.ticketing_count 0.000000 0.000002 0.241 0.809
L17.p1 -0.086044 0.038273 -2.248 0.025
L17.p2 -0.007966 0.025517 -0.312 0.755
L18.ticketing_count -0.000000 0.000002 -0.040 0.968
L18.p1 -0.111739 0.037591 -2.972 0.003
L18.p2 -0.033762 0.024537 -1.376 0.169
L19.ticketing_count -0.000001 0.000002 -0.555 0.579
L19.p1 -0.124944 0.037023 -3.375 0.001
L19.p2 -0.012871 0.022925 -0.561 0.574
L20.ticketing_count -0.000002 0.000001 -1.150 0.250
L20.p1 -0.128311 0.035752 -3.589 0.000
L20.p2 -0.020504 0.022012 -0.931 0.352
======================================================================================
Results for equation p2
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.000214 0.015836 0.014 0.989
L1.ticketing_count 0.000002 0.000002 0.842 0.400
L1.p1 -0.173026 0.059170 -2.924 0.003
L1.p2 -0.390533 0.035944 -10.865 0.000
L2.ticketing_count -0.000003 0.000003 -1.212 0.225
L2.p1 0.049973 0.061197 0.817 0.414
L2.p2 -0.420902 0.038266 -11.000 0.000
L3.ticketing_count -0.000002 0.000003 -0.764 0.445
L3.p1 -0.047697 0.062403 -0.764 0.445
L3.p2 -0.359793 0.040670 -8.847 0.000
L4.ticketing_count -0.000001 0.000003 -0.241 0.810
L4.p1 0.000511 0.063726 0.008 0.994
L4.p2 -0.375090 0.042233 -8.881 0.000
L5.ticketing_count -0.000001 0.000003 -0.248 0.804
L5.p1 -0.126026 0.064174 -1.964 0.050
L5.p2 -0.303920 0.043864 -6.929 0.000
L6.ticketing_count -0.000001 0.000003 -0.367 0.714
L6.p1 -0.045684 0.065210 -0.701 0.484
L6.p2 -0.269930 0.045100 -5.985 0.000
L7.ticketing_count 0.000000 0.000003 0.091 0.927
L7.p1 0.132487 0.065291 2.029 0.042
L7.p2 -0.001049 0.045962 -0.023 0.982
L8.ticketing_count -0.000000 0.000003 -0.056 0.955
L8.p1 -0.027626 0.065966 -0.419 0.675
L8.p2 -0.077419 0.045797 -1.690 0.091
L9.ticketing_count 0.000002 0.000003 0.515 0.606
L9.p1 -0.006636 0.065957 -0.101 0.920
L9.p2 -0.148114 0.045610 -3.247 0.001
L10.ticketing_count 0.000002 0.000003 0.530 0.596
L10.p1 -0.014039 0.066005 -0.213 0.832
L10.p2 -0.090437 0.045719 -1.978 0.048
L11.ticketing_count -0.000002 0.000003 -0.459 0.646
L11.p1 0.118658 0.065944 1.799 0.072
L11.p2 -0.116157 0.045776 -2.537 0.011
L12.ticketing_count -0.000001 0.000003 -0.351 0.725
L12.p1 0.066486 0.065889 1.009 0.313
L12.p2 -0.166486 0.045632 -3.648 0.000
L13.ticketing_count -0.000001 0.000003 -0.385 0.700
L13.p1 -0.114872 0.065888 -1.743 0.081
L13.p2 -0.101086 0.045856 -2.204 0.027
L14.ticketing_count -0.000000 0.000003 -0.072 0.943
L14.p1 0.119909 0.065523 1.830 0.067
L14.p2 0.052128 0.045986 1.134 0.257
L15.ticketing_count -0.000001 0.000003 -0.251 0.802
L15.p1 -0.060677 0.065455 -0.927 0.354
L15.p2 -0.020025 0.045023 -0.445 0.656
L16.ticketing_count -0.000000 0.000003 -0.056 0.956
L16.p1 0.011610 0.064654 0.180 0.857
L16.p2 -0.151830 0.043660 -3.478 0.001
L17.ticketing_count -0.000000 0.000003 -0.054 0.957
L17.p1 -0.132340 0.063804 -2.074 0.038
L17.p2 -0.098449 0.042539 -2.314 0.021
L18.ticketing_count -0.000001 0.000003 -0.303 0.762
L18.p1 -0.071177 0.062667 -1.136 0.256
L18.p2 -0.144206 0.040906 -3.525 0.000
L19.ticketing_count -0.000002 0.000003 -0.588 0.557
L19.p1 -0.078760 0.061721 -1.276 0.202
L19.p2 -0.156755 0.038218 -4.102 0.000
L20.ticketing_count 0.000000 0.000002 0.172 0.863
L20.p1 -0.149846 0.059602 -2.514 0.012
L20.p2 -0.098237 0.036696 -2.677 0.007
======================================================================================
Correlation matrix of residuals
ticketing_count p1 p2
ticketing_count 1.000000 0.141866 0.180670
p1 0.141866 1.000000 0.314378
p2 0.180670 0.314378 1.000000
입력한15일 차분 예측
입력한15일 차분을 더해서 원래값 예측
ticketing_count p1 p2 ticketing_count_forecasted \
date
2021/08/17 -1379.130392 0.039463 -0.867158 6142.869608
2021/08/18 7970.437498 0.030421 -0.518052 14113.307107
2021/08/19 699.704730 0.038485 -0.165383 14813.011837
2021/08/20 2127.266537 0.128304 -0.060137 16940.278374
2021/08/21 15327.708405 0.459371 1.413296 32267.986779
2021/08/22 -6374.755724 0.066170 0.696624 25893.231054
2021/08/23 -21297.623296 -0.800766 -1.538394 4595.607758
2021/08/24 3656.475441 0.085582 -0.242410 8252.083199
2021/08/25 5477.033115 -0.096167 -0.289664 13729.116314
2021/08/26 1772.842975 0.071644 -0.065844 15501.959289
2021/08/27 1385.840585 0.168161 0.130676 16887.799874
2021/08/28 15963.285158 0.442417 1.293903 32851.085032
2021/08/29 -5968.719666 0.173523 0.678578 26882.365366
2021/08/30 -20794.496932 -0.691774 -1.368857 6087.868434
2021/08/31 1866.122584 0.036551 -0.382473 7953.991018
p1_forecasted p2_forecasted
date
2021/08/17 4.605046 -1.734482
2021/08/18 4.635467 -2.252534
2021/08/19 4.673951 -2.417916
2021/08/20 4.802255 -2.478054
2021/08/21 5.261626 -1.064758
2021/08/22 5.327796 -0.368134
2021/08/23 4.527030 -1.906528
2021/08/24 4.612612 -2.148938
2021/08/25 4.516446 -2.438602
2021/08/26 4.588090 -2.504447
2021/08/27 4.756251 -2.373771
2021/08/28 5.198668 -1.079868
2021/08/29 5.372191 -0.401290
2021/08/30 4.680418 -1.770146
2021/08/31 4.716968 -2.152620
MSE: 33627318.40506824
RMSE: 5798.906656005789
Variance score: 0.783
#차분 저장
diff.to_csv("F:\\drive\\WebWorkPlace2021\\jupyter\\code\\차분.csv")#실제값과 예측값 저장
test
test.to_csv("F:\\drive\\WebWorkPlace2021\\jupyter\\code\\예매건수예측(15일).csv")#31일치 예측 수행
test,diff = get_result(df, 31)차분 전 정상성 평가
ticketing_count
ADF test statistic: -2.099553733500803
p-value: 0.24469408536639126
p1
ADF test statistic: -0.21267008715610644
p-value: 0.9369944290579096
p2
ADF test statistic: -0.9660338256671395
p-value: 0.7654502758399014
차분 플롯
차분
ticketing_count p1 p2
date
2019/01/02 -2332.0 -0.591827 -1.436540
2019/01/03 1429.0 0.050071 0.043894
2019/01/04 590.0 0.032127 -0.160157
2019/01/05 11667.0 0.453149 0.965645
2019/01/06 -5564.0 0.215044 0.535168
... ... ... ...
2021/08/27 3970.0 0.335959 0.163582
2021/08/28 25874.0 -0.250823 1.778201
2021/08/29 -13585.0 0.145392 0.699918
2021/08/30 -28219.0 -0.665134 -2.349837
2021/08/31 4930.0 0.730510 -0.323811
[973 rows x 3 columns]
차분 후 정상성 평가
ticketing_count
ADF test statistic: -8.90366524755091
p-value: 1.1532103667357817e-14
p1
ADF test statistic: -7.31633364127461
p-value: 1.2265309592959857e-10
p2
ADF test statistic: -8.654863166473886
p-value: 5.00061289751736e-14
학습, 테스트 데이터 분리
ticketing_count p1 p2
date
2019/01/02 -2332.0 -0.591827 -1.436540
2019/01/03 1429.0 0.050071 0.043894
2019/01/04 590.0 0.032127 -0.160157
2019/01/05 11667.0 0.453149 0.965645
2019/01/06 -5564.0 0.215044 0.535168
... ... ... ...
2021/07/27 7910.0 0.374974 0.102941
2021/07/28 7195.0 0.271976 -0.397341
2021/07/29 -1568.0 0.121974 0.071939
2021/07/30 1240.0 -0.323097 -0.009642
2021/07/31 11692.0 0.806326 1.693124
[942 rows x 3 columns] ticketing_count p1 p2
date
2021/08/01 -1419.0 -0.083921 0.568040
2021/08/02 -23309.0 -1.197897 -1.823121
2021/08/03 6021.0 0.382762 -0.307690
2021/08/04 5184.0 0.224371 -0.159779
2021/08/05 -1820.0 0.213700 0.045172
2021/08/06 2199.0 0.210503 0.053557
2021/08/07 15793.0 0.433313 1.324466
2021/08/08 -6053.0 0.227528 0.915187
2021/08/09 -22049.0 -1.476812 -2.419552
2021/08/10 4498.0 0.864029 -0.313854
2021/08/11 5951.0 -0.361891 0.011769
2021/08/12 -1827.0 -0.178059 -0.032996
2021/08/13 5810.0 0.349272 0.207221
2021/08/14 14591.0 0.313612 1.626783
2021/08/15 -7036.0 -0.030712 0.621387
2021/08/16 -18277.0 -0.492023 -0.229886
2021/08/17 1743.0 -0.320392 -2.102970
2021/08/18 10810.0 0.231022 -0.418104
2021/08/19 -3599.0 0.186924 0.148053
2021/08/20 1624.0 0.140931 0.255090
2021/08/21 24481.0 0.576043 2.264528
2021/08/22 -9835.0 -0.445505 0.305457
2021/08/23 -28924.0 -0.563361 -2.491265
2021/08/24 9165.0 0.635171 -0.388304
2021/08/25 9759.0 -0.326547 -0.162770
2021/08/26 -7134.0 0.101249 0.099862
2021/08/27 3970.0 0.335959 0.163582
2021/08/28 25874.0 -0.250823 1.778201
2021/08/29 -13585.0 0.145392 0.699918
2021/08/30 -28219.0 -0.665134 -2.349837
2021/08/31 4930.0 0.730510 -0.323811
VAR예측모델 생성
C:\Users\USER\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:162: ValueWarning: No frequency information was provided, so inferred frequency D will be used.
% freq, ValueWarning)
AIC 확인
[15.87563729489809, 15.557357216287832, 15.427078479994226, 15.320142887020664, 14.48060172738807, 13.815459196908648, 13.674602843731316, 13.666747066401175, 13.664793526873849, 13.677695046468395, 13.6932228219071, 13.678713838592259, 13.52530614904882, 13.491261062425483, 13.50126835271658, 13.513745267270556, 13.530328749823905, 13.543306011793984, 13.540264193133444, 13.496731046618704, 13.470470090515716, 13.488196994232275, 13.500023311385208, 13.511766957195414, 13.522155988711997, 13.538536035549402, 13.521722836728927, 13.519968653073875, 13.53152912258182]
최적값 확인
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Tue, 07, Sep, 2021
Time: 19:08:44
--------------------------------------------------------------------
No. of Equations: 3.00000 BIC: 14.4547
Nobs: 922.000 HQIC: 13.8623
Log likelihood: -9963.78 FPE: 727458.
AIC: 13.4967 Det(Omega_mle): 600261.
--------------------------------------------------------------------
Results for equation ticketing_count
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 135.454294 221.234594 0.612 0.540
L1.ticketing_count -0.510798 0.034407 -14.846 0.000
L1.p1 -1784.447639 823.130935 -2.168 0.030
L1.p2 -2826.770380 496.704652 -5.691 0.000
L2.ticketing_count -0.592900 0.038506 -15.398 0.000
L2.p1 -1269.958322 850.326719 -1.493 0.135
L2.p2 -604.709203 528.855671 -1.143 0.253
L3.ticketing_count -0.397590 0.043349 -9.172 0.000
L3.p1 -1317.606646 866.633658 -1.520 0.128
L3.p2 -1048.949905 561.963174 -1.867 0.062
L4.ticketing_count -0.260854 0.045270 -5.762 0.000
L4.p1 -382.628531 886.053692 -0.432 0.666
L4.p2 -1212.392013 583.699341 -2.077 0.038
L5.ticketing_count -0.338530 0.046262 -7.318 0.000
L5.p1 -1263.337670 891.157588 -1.418 0.156
L5.p2 -404.074380 606.186613 -0.667 0.505
L6.ticketing_count -0.104286 0.047675 -2.187 0.029
L6.p1 -644.443541 907.048346 -0.710 0.477
L6.p2 -965.143037 623.511658 -1.548 0.122
L7.ticketing_count 0.162247 0.047824 3.393 0.001
L7.p1 -425.206322 907.596583 -0.468 0.639
L7.p2 -926.225112 635.023958 -1.459 0.145
L8.ticketing_count 0.019225 0.047559 0.404 0.686
L8.p1 -396.937584 916.552241 -0.433 0.665
L8.p2 340.830832 632.590717 0.539 0.590
L9.ticketing_count -0.043038 0.047329 -0.909 0.363
L9.p1 -516.977764 916.524021 -0.564 0.573
L9.p2 -342.087069 630.096703 -0.543 0.587
L10.ticketing_count -0.101747 0.047127 -2.159 0.031
L10.p1 -272.635360 917.673841 -0.297 0.766
L10.p2 -399.453102 631.553283 -0.632 0.527
L11.ticketing_count -0.173021 0.047134 -3.671 0.000
L11.p1 -544.237686 916.661743 -0.594 0.553
L11.p2 -61.556054 632.571114 -0.097 0.922
L12.ticketing_count -0.159145 0.047497 -3.351 0.001
L12.p1 322.326402 915.851616 0.352 0.725
L12.p2 -923.789816 630.296374 -1.466 0.143
L13.ticketing_count -0.243933 0.047797 -5.103 0.000
L13.p1 -100.210598 915.813894 -0.109 0.913
L13.p2 -89.788479 633.348356 -0.142 0.887
L14.ticketing_count 0.059717 0.048175 1.240 0.215
L14.p1 -509.308398 911.364183 -0.559 0.576
L14.p2 -200.390619 634.891882 -0.316 0.752
L15.ticketing_count -0.027825 0.048068 -0.579 0.563
L15.p1 -1035.054483 913.510861 -1.133 0.257
L15.p2 363.969355 622.217002 0.585 0.559
L16.ticketing_count -0.021924 0.046580 -0.471 0.638
L16.p1 490.271300 903.091537 0.543 0.587
L16.p2 -817.396038 603.840782 -1.354 0.176
L17.ticketing_count -0.068145 0.045745 -1.490 0.136
L17.p1 -1857.094971 890.681751 -2.085 0.037
L17.p2 -326.716956 588.480118 -0.555 0.579
L18.ticketing_count -0.133925 0.043638 -3.069 0.002
L18.p1 -346.685447 874.038991 -0.397 0.692
L18.p2 -548.384527 566.530853 -0.968 0.333
L19.ticketing_count -0.128832 0.038718 -3.327 0.001
L19.p1 -1008.881602 860.967806 -1.172 0.241
L19.p2 -1055.210407 528.914092 -1.995 0.046
L20.ticketing_count -0.156058 0.033573 -4.648 0.000
L20.p1 -541.720630 831.361945 -0.652 0.515
L20.p2 -491.918515 508.820835 -0.967 0.334
======================================================================================
Results for equation p1
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.021101 0.009624 2.193 0.028
L1.ticketing_count -0.000000 0.000001 -0.049 0.961
L1.p1 -0.293984 0.035806 -8.210 0.000
L1.p2 -0.120798 0.021607 -5.591 0.000
L2.ticketing_count -0.000005 0.000002 -2.911 0.004
L2.p1 -0.211571 0.036989 -5.720 0.000
L2.p2 -0.031798 0.023005 -1.382 0.167
L3.ticketing_count -0.000003 0.000002 -1.811 0.070
L3.p1 -0.235865 0.037698 -6.257 0.000
L3.p2 -0.052685 0.024445 -2.155 0.031
L4.ticketing_count -0.000005 0.000002 -2.670 0.008
L4.p1 -0.130900 0.038543 -3.396 0.001
L4.p2 -0.072598 0.025391 -2.859 0.004
L5.ticketing_count -0.000003 0.000002 -1.362 0.173
L5.p1 -0.204512 0.038765 -5.276 0.000
L5.p2 -0.023166 0.026369 -0.879 0.380
L6.ticketing_count -0.000004 0.000002 -2.164 0.030
L6.p1 -0.107265 0.039456 -2.719 0.007
L6.p2 -0.051605 0.027123 -1.903 0.057
L7.ticketing_count -0.000003 0.000002 -1.250 0.211
L7.p1 0.157641 0.039480 3.993 0.000
L7.p2 0.004611 0.027623 0.167 0.867
L8.ticketing_count -0.000002 0.000002 -1.187 0.235
L8.p1 0.002651 0.039870 0.066 0.947
L8.p2 -0.002890 0.027518 -0.105 0.916
L9.ticketing_count -0.000004 0.000002 -1.763 0.078
L9.p1 -0.050102 0.039869 -1.257 0.209
L9.p2 -0.018999 0.027409 -0.693 0.488
L10.ticketing_count -0.000000 0.000002 -0.091 0.927
L10.p1 0.018886 0.039919 0.473 0.636
L10.p2 -0.046798 0.027472 -1.703 0.088
L11.ticketing_count -0.000002 0.000002 -0.734 0.463
L11.p1 0.030265 0.039875 0.759 0.448
L11.p2 -0.026061 0.027517 -0.947 0.344
L12.ticketing_count -0.000001 0.000002 -0.642 0.521
L12.p1 0.038584 0.039839 0.968 0.333
L12.p2 -0.042031 0.027418 -1.533 0.125
L13.ticketing_count -0.000000 0.000002 -0.230 0.818
L13.p1 0.011543 0.039838 0.290 0.772
L13.p2 -0.047724 0.027551 -1.732 0.083
L14.ticketing_count -0.000001 0.000002 -0.338 0.736
L14.p1 0.108620 0.039644 2.740 0.006
L14.p2 0.017694 0.027618 0.641 0.522
L15.ticketing_count 0.000002 0.000002 0.808 0.419
L15.p1 -0.040511 0.039738 -1.019 0.308
L15.p2 -0.007494 0.027066 -0.277 0.782
L16.ticketing_count 0.000002 0.000002 0.954 0.340
L16.p1 -0.038941 0.039284 -0.991 0.322
L16.p2 -0.046095 0.026267 -1.755 0.079
L17.ticketing_count 0.000000 0.000002 0.227 0.820
L17.p1 -0.082103 0.038745 -2.119 0.034
L17.p2 -0.009225 0.025599 -0.360 0.719
L18.ticketing_count -0.000000 0.000002 -0.102 0.918
L18.p1 -0.110973 0.038021 -2.919 0.004
L18.p2 -0.032278 0.024644 -1.310 0.190
L19.ticketing_count -0.000001 0.000002 -0.543 0.587
L19.p1 -0.130390 0.037452 -3.482 0.000
L19.p2 -0.013155 0.023008 -0.572 0.567
L20.ticketing_count -0.000002 0.000001 -1.193 0.233
L20.p1 -0.133846 0.036164 -3.701 0.000
L20.p2 -0.019621 0.022134 -0.886 0.375
======================================================================================
Results for equation p2
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.000085 0.016045 0.005 0.996
L1.ticketing_count 0.000002 0.000002 0.786 0.432
L1.p1 -0.170249 0.059697 -2.852 0.004
L1.p2 -0.391082 0.036023 -10.856 0.000
L2.ticketing_count -0.000004 0.000003 -1.254 0.210
L2.p1 0.061483 0.061669 0.997 0.319
L2.p2 -0.424604 0.038355 -11.070 0.000
L3.ticketing_count -0.000003 0.000003 -0.804 0.421
L3.p1 -0.049322 0.062852 -0.785 0.433
L3.p2 -0.361083 0.040756 -8.860 0.000
L4.ticketing_count -0.000001 0.000003 -0.258 0.796
L4.p1 0.013022 0.064260 0.203 0.839
L4.p2 -0.375584 0.042332 -8.872 0.000
L5.ticketing_count -0.000001 0.000003 -0.283 0.777
L5.p1 -0.111835 0.064630 -1.730 0.084
L5.p2 -0.304613 0.043963 -6.929 0.000
L6.ticketing_count -0.000001 0.000003 -0.302 0.763
L6.p1 -0.055170 0.065783 -0.839 0.402
L6.p2 -0.264689 0.045220 -5.853 0.000
L7.ticketing_count 0.000000 0.000003 0.062 0.951
L7.p1 0.146130 0.065823 2.220 0.026
L7.p2 0.001996 0.046055 0.043 0.965
L8.ticketing_count -0.000000 0.000003 -0.019 0.985
L8.p1 -0.032761 0.066472 -0.493 0.622
L8.p2 -0.075155 0.045878 -1.638 0.101
L9.ticketing_count 0.000002 0.000003 0.530 0.596
L9.p1 -0.006571 0.066470 -0.099 0.921
L9.p2 -0.144774 0.045697 -3.168 0.002
L10.ticketing_count 0.000002 0.000003 0.496 0.620
L10.p1 -0.020559 0.066553 -0.309 0.757
L10.p2 -0.085479 0.045803 -1.866 0.062
L11.ticketing_count -0.000001 0.000003 -0.394 0.693
L11.p1 0.103954 0.066480 1.564 0.118
L11.p2 -0.112792 0.045877 -2.459 0.014
L12.ticketing_count -0.000001 0.000003 -0.362 0.718
L12.p1 0.057462 0.066421 0.865 0.387
L12.p2 -0.162549 0.045712 -3.556 0.000
L13.ticketing_count -0.000001 0.000003 -0.378 0.706
L13.p1 -0.121417 0.066419 -1.828 0.068
L13.p2 -0.096714 0.045933 -2.106 0.035
L14.ticketing_count -0.000000 0.000003 -0.063 0.950
L14.p1 0.115739 0.066096 1.751 0.080
L14.p2 0.053432 0.046045 1.160 0.246
L15.ticketing_count -0.000001 0.000003 -0.351 0.725
L15.p1 -0.063144 0.066252 -0.953 0.341
L15.p2 -0.017982 0.045126 -0.398 0.690
L16.ticketing_count 0.000000 0.000003 0.009 0.993
L16.p1 0.001221 0.065496 0.019 0.985
L16.p2 -0.152165 0.043793 -3.475 0.001
L17.ticketing_count -0.000000 0.000003 -0.066 0.947
L17.p1 -0.131623 0.064596 -2.038 0.042
L17.p2 -0.096203 0.042679 -2.254 0.024
L18.ticketing_count -0.000001 0.000003 -0.302 0.763
L18.p1 -0.076353 0.063389 -1.205 0.228
L18.p2 -0.142043 0.041087 -3.457 0.001
L19.ticketing_count -0.000002 0.000003 -0.623 0.533
L19.p1 -0.084186 0.062441 -1.348 0.178
L19.p2 -0.154012 0.038359 -4.015 0.000
L20.ticketing_count 0.000000 0.000002 0.097 0.923
L20.p1 -0.153419 0.060294 -2.545 0.011
L20.p2 -0.098803 0.036902 -2.677 0.007
======================================================================================
Correlation matrix of residuals
ticketing_count p1 p2
ticketing_count 1.000000 0.145215 0.180840
p1 0.145215 1.000000 0.313024
p2 0.180840 0.313024 1.000000
입력한31일 차분 예측
입력한31일 차분을 더해서 원래값 예측
ticketing_count p1 p2 ticketing_count_forecasted \
date
2021/08/01 -7672.161584 0.240809 0.652724 21592.838416
2021/08/02 -19234.859185 -1.139989 -2.021386 2357.979231
2021/08/03 6614.862081 0.141454 -0.163036 8972.841312
2021/08/04 6160.817413 0.217028 -0.094239 15133.658726
2021/08/05 -2617.425303 0.116703 -0.052759 12516.233422
2021/08/06 4547.255332 0.012815 0.139671 17063.488755
2021/08/07 14845.387190 0.548608 1.459166 31908.875945
2021/08/08 -9267.871208 0.236678 0.727491 22641.004737
2021/08/09 -20279.112519 -1.089021 -1.994745 2361.892218
2021/08/10 7840.176624 0.106145 -0.210485 10202.068842
2021/08/11 4442.480825 0.144063 0.001570 14644.549667
2021/08/12 -835.785365 0.118991 0.008042 13808.764302
2021/08/13 3614.169622 0.043069 -0.000119 17422.933924
2021/08/14 15654.777920 0.593197 1.458439 33077.711844
2021/08/15 -11381.048597 0.213163 0.667913 21696.663247
2021/08/16 -18757.621828 -1.036709 -1.862996 2939.041418
2021/08/17 6856.851281 0.073523 -0.273500 9795.892700
2021/08/18 6065.562289 0.117522 0.062516 15861.454989
2021/08/19 -2108.765441 0.091566 -0.046614 13752.689547
2021/08/20 3696.115208 0.000053 -0.044116 17448.804755
2021/08/21 15445.662302 0.538021 1.412922 32894.467058
2021/08/22 -10999.674744 0.189025 0.634848 21894.792313
2021/08/23 -19159.381682 -0.918728 -1.756677 2735.410632
2021/08/24 7203.628853 0.030576 -0.301209 9939.039484
2021/08/25 5461.315648 0.121360 0.111626 15400.355132
2021/08/26 -2017.323642 0.070288 -0.063346 13383.031489
2021/08/27 3781.523170 0.036569 -0.025455 17164.554659
2021/08/28 15917.544589 0.514517 1.352018 33082.099248
2021/08/29 -11288.407714 0.188214 0.610122 21793.691534
2021/08/30 -19093.107271 -0.874509 -1.676044 2700.584262
2021/08/31 6938.956752 0.009714 -0.326189 9639.541015
p1_forecasted p2_forecasted
date
2021/08/01 5.408619 -0.301303
2021/08/02 4.268630 -2.322689
2021/08/03 4.410084 -2.485725
2021/08/04 4.627113 -2.579965
2021/08/05 4.743815 -2.632723
2021/08/06 4.756630 -2.493053
2021/08/07 5.305238 -1.033886
2021/08/08 5.541916 -0.306395
2021/08/09 4.452895 -2.301140
2021/08/10 4.559040 -2.511625
2021/08/11 4.703103 -2.510055
2021/08/12 4.822095 -2.502012
2021/08/13 4.865163 -2.502131
2021/08/14 5.458360 -1.043692
2021/08/15 5.671523 -0.375779
2021/08/16 4.634814 -2.238775
2021/08/17 4.708337 -2.512275
2021/08/18 4.825858 -2.449760
2021/08/19 4.917424 -2.496374
2021/08/20 4.917477 -2.540490
2021/08/21 5.455498 -1.127567
2021/08/22 5.644523 -0.492719
2021/08/23 4.725795 -2.249396
2021/08/24 4.756371 -2.550605
2021/08/25 4.877731 -2.438979
2021/08/26 4.948019 -2.502325
2021/08/27 4.984588 -2.527780
2021/08/28 5.499105 -1.175762
2021/08/29 5.687319 -0.565640
2021/08/30 4.812810 -2.241684
2021/08/31 4.822524 -2.567873
MSE: 21794710.70737103
RMSE: 4668.480556602012
Variance score: 0.819
#31일치 예측 수행 저장
test
test.to_csv("F:\\drive\\WebWorkPlace2021\\jupyter\\code\\예매건수예측(30일).csv")#62일치 예측 수행 저장
test,diff = get_result(df, 62)
test.to_csv("F:\\drive\\WebWorkPlace2021\\jupyter\\code\\예매건수예측(60일).csv")차분 전 정상성 평가
ticketing_count
ADF test statistic: -2.099553733500803
p-value: 0.24469408536639126
p1
ADF test statistic: -0.21267008715610644
p-value: 0.9369944290579096
p2
ADF test statistic: -0.9660338256671395
p-value: 0.7654502758399014
차분 플롯
차분
ticketing_count p1 p2
date
2019/01/02 -2332.0 -0.591827 -1.436540
2019/01/03 1429.0 0.050071 0.043894
2019/01/04 590.0 0.032127 -0.160157
2019/01/05 11667.0 0.453149 0.965645
2019/01/06 -5564.0 0.215044 0.535168
... ... ... ...
2021/08/27 3970.0 0.335959 0.163582
2021/08/28 25874.0 -0.250823 1.778201
2021/08/29 -13585.0 0.145392 0.699918
2021/08/30 -28219.0 -0.665134 -2.349837
2021/08/31 4930.0 0.730510 -0.323811
[973 rows x 3 columns]
차분 후 정상성 평가
ticketing_count
ADF test statistic: -8.90366524755091
p-value: 1.1532103667357817e-14
p1
ADF test statistic: -7.31633364127461
p-value: 1.2265309592959857e-10
p2
ADF test statistic: -8.654863166473886
p-value: 5.00061289751736e-14
학습, 테스트 데이터 분리
ticketing_count p1 p2
date
2019/01/02 -2332.0 -0.591827 -1.436540
2019/01/03 1429.0 0.050071 0.043894
2019/01/04 590.0 0.032127 -0.160157
2019/01/05 11667.0 0.453149 0.965645
2019/01/06 -5564.0 0.215044 0.535168
... ... ... ...
2021/06/26 30644.0 0.388360 1.257844
2021/06/27 -14777.0 0.219874 0.414628
2021/06/28 -33224.0 -0.868161 -1.371930
2021/06/29 6803.0 0.379811 -0.154252
2021/06/30 9164.0 0.049356 0.075260
[911 rows x 3 columns] ticketing_count p1 p2
date
2021/07/01 -7908.0 -0.539226 -1.429798
2021/07/02 6262.0 0.176894 -0.161103
2021/07/03 25836.0 0.896067 1.906416
2021/07/04 -11935.0 0.063430 0.781533
2021/07/05 -28317.0 -1.381765 -2.619479
2021/07/06 4418.0 0.438984 -0.023562
2021/07/07 6981.0 0.296948 -0.196619
2021/07/08 -74.0 0.549223 0.449346
2021/07/09 3952.0 0.168500 -0.055949
2021/07/10 12881.0 0.569024 1.851338
2021/07/11 -8866.0 0.812047 0.715844
2021/07/12 -20980.0 -1.441237 -2.704677
2021/07/13 5282.0 0.127563 -0.151346
2021/07/14 6800.0 0.162232 -0.473255
2021/07/15 -2160.0 0.609734 0.405506
2021/07/16 6785.0 -0.158652 0.071389
2021/07/17 15670.0 -0.040378 1.892517
2021/07/18 -10874.0 0.183432 0.536379
2021/07/19 -21186.0 -1.216572 -2.196799
2021/07/20 5835.0 -0.037867 -0.044979
2021/07/21 5728.0 0.508241 -0.487273
2021/07/22 -2299.0 0.052761 0.123404
2021/07/23 6696.0 0.009651 0.157808
2021/07/24 18329.0 0.402513 1.595068
2021/07/25 -10669.0 0.279351 0.608926
2021/07/26 -23269.0 -1.756048 -1.957582
2021/07/27 7910.0 0.374974 0.102941
2021/07/28 7195.0 0.271976 -0.397341
2021/07/29 -1568.0 0.121974 0.071939
2021/07/30 1240.0 -0.323097 -0.009642
2021/07/31 11692.0 0.806326 1.693124
2021/08/01 -1419.0 -0.083921 0.568040
2021/08/02 -23309.0 -1.197897 -1.823121
2021/08/03 6021.0 0.382762 -0.307690
2021/08/04 5184.0 0.224371 -0.159779
2021/08/05 -1820.0 0.213700 0.045172
2021/08/06 2199.0 0.210503 0.053557
2021/08/07 15793.0 0.433313 1.324466
2021/08/08 -6053.0 0.227528 0.915187
2021/08/09 -22049.0 -1.476812 -2.419552
2021/08/10 4498.0 0.864029 -0.313854
2021/08/11 5951.0 -0.361891 0.011769
2021/08/12 -1827.0 -0.178059 -0.032996
2021/08/13 5810.0 0.349272 0.207221
2021/08/14 14591.0 0.313612 1.626783
2021/08/15 -7036.0 -0.030712 0.621387
2021/08/16 -18277.0 -0.492023 -0.229886
2021/08/17 1743.0 -0.320392 -2.102970
2021/08/18 10810.0 0.231022 -0.418104
2021/08/19 -3599.0 0.186924 0.148053
2021/08/20 1624.0 0.140931 0.255090
2021/08/21 24481.0 0.576043 2.264528
2021/08/22 -9835.0 -0.445505 0.305457
2021/08/23 -28924.0 -0.563361 -2.491265
2021/08/24 9165.0 0.635171 -0.388304
2021/08/25 9759.0 -0.326547 -0.162770
2021/08/26 -7134.0 0.101249 0.099862
2021/08/27 3970.0 0.335959 0.163582
2021/08/28 25874.0 -0.250823 1.778201
2021/08/29 -13585.0 0.145392 0.699918
2021/08/30 -28219.0 -0.665134 -2.349837
2021/08/31 4930.0 0.730510 -0.323811
VAR예측모델 생성
C:\Users\USER\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:162: ValueWarning: No frequency information was provided, so inferred frequency D will be used.
% freq, ValueWarning)
AIC 확인
[15.857825738516011, 15.542765651905297, 15.416434613693978, 15.315725629221138, 14.474354911441312, 13.81901076157632, 13.680811498907984, 13.672516129265682, 13.668377252947026, 13.681474829447378, 13.698663519856442, 13.683979739128665, 13.52618800172015, 13.493105460365054, 13.501008939252996, 13.512567749870824, 13.528482798591906, 13.541057001235757, 13.541583886114829, 13.49979072634149, 13.476690198062512, 13.495914939128362, 13.506418609340054, 13.518002310623492, 13.528839763077556, 13.54571761470332, 13.527668591127455, 13.525978459103547, 13.537559244205145]
최적값 확인
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Tue, 07, Sep, 2021
Time: 19:08:47
--------------------------------------------------------------------
No. of Equations: 3.00000 BIC: 14.4841
Nobs: 891.000 HQIC: 13.8760
Log likelihood: -9623.98 FPE: 729733.
AIC: 13.4998 Det(Omega_mle): 598255.
--------------------------------------------------------------------
Results for equation ticketing_count
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 182.415005 226.050794 0.807 0.420
L1.ticketing_count -0.507607 0.034997 -14.504 0.000
L1.p1 -1963.674250 855.243540 -2.296 0.022
L1.p2 -2982.657945 510.765204 -5.840 0.000
L2.ticketing_count -0.589704 0.039157 -15.060 0.000
L2.p1 -1253.516652 887.091315 -1.413 0.158
L2.p2 -653.645229 545.854982 -1.197 0.231
L3.ticketing_count -0.397659 0.044031 -9.031 0.000
L3.p1 -1109.836477 904.163434 -1.227 0.220
L3.p2 -1092.667186 580.947954 -1.881 0.060
L4.ticketing_count -0.264568 0.045969 -5.755 0.000
L4.p1 -217.964626 925.257714 -0.236 0.814
L4.p2 -1299.225958 603.133012 -2.154 0.031
L5.ticketing_count -0.336628 0.046979 -7.166 0.000
L5.p1 -1535.347598 932.650252 -1.646 0.100
L5.p2 -594.066495 627.580977 -0.947 0.344
L6.ticketing_count -0.096086 0.048402 -1.985 0.047
L6.p1 -730.384618 947.252195 -0.771 0.441
L6.p2 -1146.450040 647.043643 -1.772 0.076
L7.ticketing_count 0.160800 0.048490 3.316 0.001
L7.p1 -270.108129 946.019983 -0.286 0.775
L7.p2 -1019.057272 660.970318 -1.542 0.123
L8.ticketing_count 0.016185 0.048220 0.336 0.737
L8.p1 -277.571929 955.693044 -0.290 0.771
L8.p2 185.285404 657.842817 0.282 0.778
L9.ticketing_count -0.046925 0.047973 -0.978 0.328
L9.p1 -540.533657 955.436908 -0.566 0.572
L9.p2 -584.613659 655.036170 -0.892 0.372
L10.ticketing_count -0.101683 0.047761 -2.129 0.033
L10.p1 -270.340581 955.777374 -0.283 0.777
L10.p2 -603.696531 657.510552 -0.918 0.359
L11.ticketing_count -0.175075 0.047759 -3.666 0.000
L11.p1 -453.889348 955.338650 -0.475 0.635
L11.p2 -165.051357 657.845866 -0.251 0.802
L12.ticketing_count -0.163646 0.048147 -3.399 0.001
L12.p1 437.460615 956.397022 0.457 0.647
L12.p2 -1000.294818 654.942968 -1.527 0.127
L13.ticketing_count -0.244635 0.048475 -5.047 0.000
L13.p1 -189.008349 956.415513 -0.198 0.843
L13.p2 -211.809296 658.430821 -0.322 0.748
L14.ticketing_count 0.065789 0.048886 1.346 0.178
L14.p1 -786.160421 950.565808 -0.827 0.408
L14.p2 -314.843714 660.868701 -0.476 0.634
L15.ticketing_count -0.022360 0.048803 -0.458 0.647
L15.p1 -1042.730939 951.202213 -1.096 0.273
L15.p2 333.229924 645.454660 0.516 0.606
L16.ticketing_count -0.019838 0.047306 -0.419 0.675
L16.p1 674.513831 939.189592 0.718 0.473
L16.p2 -882.713310 624.144007 -1.414 0.157
L17.ticketing_count -0.064715 0.046498 -1.392 0.164
L17.p1 -2005.577285 924.726381 -2.169 0.030
L17.p2 -456.501973 606.537953 -0.753 0.452
L18.ticketing_count -0.129079 0.044416 -2.906 0.004
L18.p1 -425.894224 907.278730 -0.469 0.639
L18.p2 -702.569171 583.106596 -1.205 0.228
L19.ticketing_count -0.123502 0.039436 -3.132 0.002
L19.p1 -1083.236946 893.072027 -1.213 0.225
L19.p2 -1103.704648 543.553690 -2.031 0.042
L20.ticketing_count -0.157841 0.034150 -4.622 0.000
L20.p1 -469.416448 859.066270 -0.546 0.585
L20.p2 -512.443612 520.598394 -0.984 0.325
======================================================================================
Results for equation p1
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.020434 0.009635 2.121 0.034
L1.ticketing_count 0.000000 0.000001 0.321 0.748
L1.p1 -0.302710 0.036452 -8.304 0.000
L1.p2 -0.116750 0.021770 -5.363 0.000
L2.ticketing_count -0.000005 0.000002 -2.876 0.004
L2.p1 -0.205102 0.037809 -5.425 0.000
L2.p2 -0.018335 0.023265 -0.788 0.431
L3.ticketing_count -0.000003 0.000002 -1.838 0.066
L3.p1 -0.238664 0.038537 -6.193 0.000
L3.p2 -0.044503 0.024761 -1.797 0.072
L4.ticketing_count -0.000005 0.000002 -2.568 0.010
L4.p1 -0.146800 0.039436 -3.722 0.000
L4.p2 -0.069467 0.025707 -2.702 0.007
L5.ticketing_count -0.000002 0.000002 -1.024 0.306
L5.p1 -0.208316 0.039751 -5.240 0.000
L5.p2 -0.023135 0.026749 -0.865 0.387
L6.ticketing_count -0.000004 0.000002 -1.981 0.048
L6.p1 -0.105356 0.040374 -2.610 0.009
L6.p2 -0.040138 0.027578 -1.455 0.146
L7.ticketing_count -0.000003 0.000002 -1.303 0.193
L7.p1 0.159834 0.040321 3.964 0.000
L7.p2 0.013835 0.028172 0.491 0.623
L8.ticketing_count -0.000002 0.000002 -1.151 0.250
L8.p1 0.007706 0.040733 0.189 0.850
L8.p2 0.000464 0.028038 0.017 0.987
L9.ticketing_count -0.000003 0.000002 -1.647 0.100
L9.p1 -0.047906 0.040722 -1.176 0.239
L9.p2 -0.009509 0.027919 -0.341 0.733
L10.ticketing_count 0.000000 0.000002 0.008 0.994
L10.p1 0.032141 0.040737 0.789 0.430
L10.p2 -0.033664 0.028024 -1.201 0.230
L11.ticketing_count -0.000002 0.000002 -0.775 0.438
L11.p1 0.058923 0.040718 1.447 0.148
L11.p2 -0.020162 0.028039 -0.719 0.472
L12.ticketing_count -0.000001 0.000002 -0.594 0.553
L12.p1 0.041130 0.040763 1.009 0.313
L12.p2 -0.037590 0.027915 -1.347 0.178
L13.ticketing_count -0.000000 0.000002 -0.042 0.967
L13.p1 0.017023 0.040764 0.418 0.676
L13.p2 -0.045530 0.028063 -1.622 0.105
L14.ticketing_count -0.000000 0.000002 -0.175 0.861
L14.p1 0.123573 0.040515 3.050 0.002
L14.p2 0.022608 0.028167 0.803 0.422
L15.ticketing_count 0.000002 0.000002 0.795 0.427
L15.p1 -0.018930 0.040542 -0.467 0.641
L15.p2 -0.001931 0.027510 -0.070 0.944
L16.ticketing_count 0.000002 0.000002 1.005 0.315
L16.p1 -0.033586 0.040030 -0.839 0.401
L16.p2 -0.046662 0.026602 -1.754 0.079
L17.ticketing_count 0.000001 0.000002 0.435 0.663
L17.p1 -0.082311 0.039413 -2.088 0.037
L17.p2 -0.014665 0.025852 -0.567 0.571
L18.ticketing_count 0.000000 0.000002 0.116 0.908
L18.p1 -0.100182 0.038670 -2.591 0.010
L18.p2 -0.030587 0.024853 -1.231 0.218
L19.ticketing_count -0.000001 0.000002 -0.696 0.486
L19.p1 -0.109616 0.038064 -2.880 0.004
L19.p2 -0.007205 0.023167 -0.311 0.756
L20.ticketing_count -0.000002 0.000001 -1.336 0.181
L20.p1 -0.120062 0.036615 -3.279 0.001
L20.p2 -0.017823 0.022189 -0.803 0.422
======================================================================================
Results for equation p2
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.005774 0.016188 0.357 0.721
L1.ticketing_count 0.000002 0.000003 0.843 0.399
L1.p1 -0.178003 0.061247 -2.906 0.004
L1.p2 -0.400148 0.036578 -10.940 0.000
L2.ticketing_count -0.000004 0.000003 -1.289 0.197
L2.p1 0.072988 0.063528 1.149 0.251
L2.p2 -0.428868 0.039091 -10.971 0.000
L3.ticketing_count -0.000004 0.000003 -1.150 0.250
L3.p1 -0.024023 0.064750 -0.371 0.711
L3.p2 -0.364706 0.041604 -8.766 0.000
L4.ticketing_count -0.000002 0.000003 -0.518 0.605
L4.p1 0.014991 0.066261 0.226 0.821
L4.p2 -0.391022 0.043192 -9.053 0.000
L5.ticketing_count -0.000001 0.000003 -0.303 0.762
L5.p1 -0.102746 0.066790 -1.538 0.124
L5.p2 -0.323041 0.044943 -7.188 0.000
L6.ticketing_count -0.000001 0.000003 -0.396 0.692
L6.p1 -0.036594 0.067836 -0.539 0.590
L6.p2 -0.278674 0.046337 -6.014 0.000
L7.ticketing_count -0.000000 0.000003 -0.039 0.969
L7.p1 0.171858 0.067748 2.537 0.011
L7.p2 -0.024274 0.047334 -0.513 0.608
L8.ticketing_count -0.000001 0.000003 -0.174 0.862
L8.p1 -0.017281 0.068441 -0.252 0.801
L8.p2 -0.100663 0.047110 -2.137 0.033
L9.ticketing_count 0.000002 0.000003 0.605 0.545
L9.p1 -0.000261 0.068422 -0.004 0.997
L9.p2 -0.172523 0.046909 -3.678 0.000
L10.ticketing_count 0.000002 0.000003 0.534 0.594
L10.p1 -0.007682 0.068447 -0.112 0.911
L10.p2 -0.105107 0.047087 -2.232 0.026
L11.ticketing_count -0.000001 0.000003 -0.437 0.662
L11.p1 0.118395 0.068415 1.731 0.084
L11.p2 -0.133158 0.047111 -2.827 0.005
L12.ticketing_count -0.000001 0.000003 -0.350 0.726
L12.p1 0.053334 0.068491 0.779 0.436
L12.p2 -0.185461 0.046903 -3.954 0.000
L13.ticketing_count -0.000001 0.000003 -0.314 0.754
L13.p1 -0.120939 0.068492 -1.766 0.077
L13.p2 -0.121771 0.047153 -2.582 0.010
L14.ticketing_count -0.000000 0.000004 -0.018 0.986
L14.p1 0.122216 0.068073 1.795 0.073
L14.p2 0.034106 0.047327 0.721 0.471
L15.ticketing_count -0.000001 0.000003 -0.418 0.676
L15.p1 -0.046828 0.068119 -0.687 0.492
L15.p2 -0.027047 0.046223 -0.585 0.558
L16.ticketing_count 0.000000 0.000003 0.008 0.993
L16.p1 -0.011780 0.067259 -0.175 0.861
L16.p2 -0.162745 0.044697 -3.641 0.000
L17.ticketing_count 0.000000 0.000003 0.125 0.901
L17.p1 -0.153040 0.066223 -2.311 0.021
L17.p2 -0.111207 0.043436 -2.560 0.010
L18.ticketing_count 0.000000 0.000003 0.068 0.945
L18.p1 -0.086382 0.064973 -1.329 0.184
L18.p2 -0.148481 0.041758 -3.556 0.000
L19.ticketing_count -0.000001 0.000003 -0.529 0.597
L19.p1 -0.076089 0.063956 -1.190 0.234
L19.p2 -0.153489 0.038926 -3.943 0.000
L20.ticketing_count 0.000000 0.000002 0.203 0.839
L20.p1 -0.155479 0.061521 -2.527 0.011
L20.p2 -0.103581 0.037282 -2.778 0.005
======================================================================================
Correlation matrix of residuals
ticketing_count p1 p2
ticketing_count 1.000000 0.150109 0.182189
p1 0.150109 1.000000 0.307075
p2 0.182189 0.307075 1.000000
입력한62일 차분 예측
입력한62일 차분을 더해서 원래값 예측
ticketing_count p1 p2 ticketing_count_forecasted \
date
2021/07/01 -2366.997484 0.113782 0.040787 17511.002516
2021/07/02 3833.721085 -0.046023 -0.006136 21344.723601
2021/07/03 26999.836743 0.298639 1.033577 48344.560344
2021/07/04 -13284.011934 0.215947 0.504627 35060.548410
2021/07/05 -32183.487924 -0.862973 -1.573394 2877.060486
2021/07/06 9658.515851 0.181953 0.038405 12535.576337
2021/07/07 5316.632150 0.047107 -0.087693 17852.208488
2021/07/08 -476.575575 0.170688 0.138565 17375.632913
2021/07/09 4126.809111 0.054583 0.086048 21502.442024
2021/07/10 27094.705933 0.409034 1.098334 48597.147957
2021/07/11 -14051.007599 0.229965 0.449575 34546.140358
2021/07/12 -29126.363568 -0.866764 -1.529143 5419.776790
2021/07/13 6495.878190 0.106112 -0.049769 11915.654980
2021/07/14 6503.638647 -0.029069 -0.132196 18419.293627
2021/07/15 -714.975625 0.157772 0.090307 17704.318001
2021/07/16 3839.080759 -0.013672 -0.010577 21543.398760
2021/07/17 25590.214999 0.424779 1.044545 47133.613759
2021/07/18 -12329.710927 0.216791 0.436696 34803.902832
2021/07/19 -28986.190510 -0.807407 -1.458906 5817.712322
2021/07/20 6374.998342 0.076599 -0.054232 12192.710664
2021/07/21 6985.820291 0.031220 -0.011946 19178.530955
2021/07/22 -1352.377754 0.121078 0.045189 17826.153201
2021/07/23 4217.353433 0.028962 0.042847 22043.506635
2021/07/24 24420.295789 0.408389 1.012892 46463.802423
2021/07/25 -12096.724855 0.212282 0.394889 34367.077569
2021/07/26 -27914.914966 -0.799221 -1.424805 6452.162602
2021/07/27 5826.234351 0.033171 -0.111846 12278.396953
2021/07/28 6725.317441 0.018539 -0.007225 19003.714394
2021/07/29 -1212.655844 0.107560 0.044138 17791.058549
2021/07/30 4424.537577 0.042048 0.071379 22215.596126
2021/07/31 23768.864654 0.436189 1.036518 45984.460781
2021/08/01 -11761.888940 0.219779 0.411291 34222.571841
2021/08/02 -26754.818784 -0.753541 -1.365121 7467.753058
2021/08/03 5044.840942 0.010209 -0.142776 12512.593999
2021/08/04 6901.000552 0.021827 0.007042 19413.594551
2021/08/05 -1437.327958 0.088392 0.008548 17976.266593
2021/08/06 4634.995460 0.038329 0.056782 22611.262052
2021/08/07 22616.931159 0.428393 1.007337 45228.193212
2021/08/08 -11143.823104 0.206121 0.390403 34084.370107
2021/08/09 -26070.030930 -0.723002 -1.319727 8014.339178
2021/08/10 4701.300153 -0.012245 -0.168318 12715.639330
2021/08/11 6896.855915 0.036731 0.035340 19612.495245
2021/08/12 -1429.249842 0.079666 0.000541 18183.245404
2021/08/13 4744.685979 0.050520 0.072811 22927.931383
2021/08/14 21838.898501 0.428563 0.997045 44766.829884
2021/08/15 -10819.569570 0.200088 0.379782 33947.260315
2021/08/16 -25270.624740 -0.696982 -1.282748 8676.635575
2021/08/17 4170.880013 -0.034892 -0.207988 12847.515588
2021/08/18 6869.950390 0.037637 0.038781 19717.465978
2021/08/19 -1514.512339 0.065638 -0.018953 18202.953639
2021/08/20 4916.620621 0.051873 0.077705 23119.574260
2021/08/21 21074.666390 0.427732 0.990731 44194.240650
2021/08/22 -10388.074586 0.194098 0.381297 33806.166064
2021/08/23 -24533.493171 -0.665299 -1.231414 9272.672893
2021/08/24 3781.285349 -0.049017 -0.227735 13053.958242
2021/08/25 6857.917700 0.045484 0.051382 19911.875942
2021/08/26 -1557.157325 0.056682 -0.032935 18354.718617
2021/08/27 5039.541576 0.054656 0.077764 23394.260194
2021/08/28 20312.201600 0.421941 0.973380 43706.461794
2021/08/29 -10014.893444 0.184628 0.370590 33691.568350
2021/08/30 -23874.739648 -0.640779 -1.191611 9816.828702
2021/08/31 3412.998821 -0.064704 -0.251412 13229.827523
p1_forecasted p2_forecasted
date
2021/07/01 4.294587 -0.967315
2021/07/02 4.248563 -0.973450
2021/07/03 4.547202 0.060127
2021/07/04 4.763149 0.564754
2021/07/05 3.900176 -1.008640
2021/07/06 4.082129 -0.970235
2021/07/07 4.129236 -1.057928
2021/07/08 4.299924 -0.919363
2021/07/09 4.354506 -0.833314
2021/07/10 4.763540 0.265020
2021/07/11 4.993505 0.714594
2021/07/12 4.126742 -0.814549
2021/07/13 4.232853 -0.864318
2021/07/14 4.203784 -0.996515
2021/07/15 4.361557 -0.906208
2021/07/16 4.347885 -0.916785
2021/07/17 4.772663 0.127760
2021/07/18 4.989454 0.564456
2021/07/19 4.182047 -0.894450
2021/07/20 4.258646 -0.948682
2021/07/21 4.289866 -0.960628
2021/07/22 4.410944 -0.915440
2021/07/23 4.439906 -0.872593
2021/07/24 4.848294 0.140299
2021/07/25 5.060576 0.535188
2021/07/26 4.261356 -0.889617
2021/07/27 4.294527 -1.001464
2021/07/28 4.313066 -1.008688
2021/07/29 4.420625 -0.964550
2021/07/30 4.462673 -0.893171
2021/07/31 4.898862 0.143346
2021/08/01 5.118641 0.554637
2021/08/02 4.365100 -0.810484
2021/08/03 4.375309 -0.953260
2021/08/04 4.397136 -0.946218
2021/08/05 4.485528 -0.937670
2021/08/06 4.523857 -0.880887
2021/08/07 4.952250 0.126450
2021/08/08 5.158371 0.516853
2021/08/09 4.435369 -0.802875
2021/08/10 4.423124 -0.971192
2021/08/11 4.459855 -0.935852
2021/08/12 4.539521 -0.935311
2021/08/13 4.590040 -0.862500
2021/08/14 5.018603 0.134545
2021/08/15 5.218692 0.514327
2021/08/16 4.521710 -0.768420
2021/08/17 4.486818 -0.976408
2021/08/18 4.524454 -0.937627
2021/08/19 4.590092 -0.956580
2021/08/20 4.641965 -0.878875
2021/08/21 5.069697 0.111856
2021/08/22 5.263795 0.493153
2021/08/23 4.598495 -0.738261
2021/08/24 4.549478 -0.965996
2021/08/25 4.594962 -0.914614
2021/08/26 4.651643 -0.947550
2021/08/27 4.706299 -0.869785
2021/08/28 5.128240 0.103595
2021/08/29 5.312869 0.474185
2021/08/30 4.672090 -0.717426
2021/08/31 4.607386 -0.968837
MSE: 37797203.83872618
RMSE: 6147.943057537715
Variance score: 0.672
#92일치 예측 수행 저장
test,diff = get_result(df,92)
test.to_csv("F:\\drive\\WebWorkPlace2021\\jupyter\\code\\예매건수예측(90일).csv")차분 전 정상성 평가
ticketing_count
ADF test statistic: -2.099553733500803
p-value: 0.24469408536639126
p1
ADF test statistic: -0.212670087154636
p-value: 0.9369944290580889
p2
ADF test statistic: -0.9660338245209754
p-value: 0.7654502762442534
차분 플롯
차분
ticketing_count p1 p2
date
2019/01/02 -2332.0 -0.591827 -1.436540
2019/01/03 1429.0 0.050071 0.043894
2019/01/04 590.0 0.032127 -0.160157
2019/01/05 11667.0 0.453149 0.965645
2019/01/06 -5564.0 0.215044 0.535168
... ... ... ...
2021/08/27 3970.0 0.335959 0.163582
2021/08/28 25874.0 -0.250823 1.778201
2021/08/29 -13585.0 0.145392 0.699918
2021/08/30 -28219.0 -0.665134 -2.349837
2021/08/31 4930.0 0.730510 -0.323811
[973 rows x 3 columns]
차분 후 정상성 평가
ticketing_count
ADF test statistic: -8.90366524755091
p-value: 1.1532103667357817e-14
p1
ADF test statistic: -7.316333641277955
p-value: 1.2265309592725403e-10
p2
ADF test statistic: -8.654863156313569
p-value: 5.000613197077801e-14
학습, 테스트 데이터 분리
ticketing_count p1 p2
date
2019/01/02 -2332.0 -0.591827 -1.436540
2019/01/03 1429.0 0.050071 0.043894
2019/01/04 590.0 0.032127 -0.160157
2019/01/05 11667.0 0.453149 0.965645
2019/01/06 -5564.0 0.215044 0.535168
... ... ... ...
2021/05/27 -655.0 0.025590 0.119197
2021/05/28 2831.0 -0.018036 0.093058
2021/05/29 25488.0 0.096412 1.349614
2021/05/30 -15747.0 0.175624 0.389396
2021/05/31 -27213.0 -0.680565 -1.806781
[881 rows x 3 columns] ticketing_count p1 p2
date
2021/06/01 3597.0 0.504558 0.212486
2021/06/02 8386.0 -0.045186 -0.239367
2021/06/03 -3497.0 0.563131 0.101536
2021/06/04 5764.0 -0.177182 0.128011
2021/06/05 20849.0 0.185960 1.329411
2021/06/06 -10705.0 0.084717 0.494169
2021/06/07 -24991.0 -0.638139 -1.491230
2021/06/08 6444.0 0.201814 -0.185252
2021/06/09 5223.0 0.077752 -0.080977
2021/06/10 -2713.0 0.091582 0.015239
2021/06/11 7851.0 -0.122185 -0.105750
2021/06/12 20693.0 0.184376 1.373220
2021/06/13 -12655.0 0.211366 0.463448
2021/06/14 -25960.0 -0.961899 -1.813409
2021/06/15 7420.0 0.442599 0.205563
2021/06/16 5540.0 0.139153 0.041923
2021/06/17 2553.0 0.089577 -0.113050
2021/06/18 4035.0 0.398201 0.144201
2021/06/19 29447.0 -0.185092 1.139898
2021/06/20 -15918.0 0.162038 0.519437
2021/06/21 -31806.0 -1.126666 -1.680110
2021/06/22 9669.0 0.294151 -0.128855
2021/06/23 4698.0 -0.048134 -0.489230
2021/06/24 659.0 0.466503 0.180801
2021/06/25 2911.0 -0.306558 -0.104249
2021/06/26 30644.0 0.388360 1.257844
2021/06/27 -14777.0 0.219874 0.414628
2021/06/28 -33224.0 -0.868161 -1.371930
2021/06/29 6803.0 0.379811 -0.154252
2021/06/30 9164.0 0.049356 0.075260
2021/07/01 -7908.0 -0.539226 -1.429798
2021/07/02 6262.0 0.176894 -0.161103
2021/07/03 25836.0 0.896067 1.906416
2021/07/04 -11935.0 0.063430 0.781533
2021/07/05 -28317.0 -1.381765 -2.619479
2021/07/06 4418.0 0.438984 -0.023562
2021/07/07 6981.0 0.296948 -0.196619
2021/07/08 -74.0 0.549223 0.449346
2021/07/09 3952.0 0.168500 -0.055949
2021/07/10 12881.0 0.569024 1.851338
2021/07/11 -8866.0 0.812047 0.715844
2021/07/12 -20980.0 -1.441237 -2.704677
2021/07/13 5282.0 0.127563 -0.151346
2021/07/14 6800.0 0.162232 -0.473255
2021/07/15 -2160.0 0.609734 0.405506
2021/07/16 6785.0 -0.158652 0.071389
2021/07/17 15670.0 -0.040378 1.892517
2021/07/18 -10874.0 0.183432 0.536379
2021/07/19 -21186.0 -1.216572 -2.196799
2021/07/20 5835.0 -0.037867 -0.044979
2021/07/21 5728.0 0.508241 -0.487273
2021/07/22 -2299.0 0.052761 0.123404
2021/07/23 6696.0 0.009651 0.157808
2021/07/24 18329.0 0.402513 1.595068
2021/07/25 -10669.0 0.279351 0.608926
2021/07/26 -23269.0 -1.756048 -1.957582
2021/07/27 7910.0 0.374974 0.102941
2021/07/28 7195.0 0.271976 -0.397341
2021/07/29 -1568.0 0.121974 0.071939
2021/07/30 1240.0 -0.323097 -0.009642
2021/07/31 11692.0 0.806326 1.693124
2021/08/01 -1419.0 -0.083921 0.568040
2021/08/02 -23309.0 -1.197897 -1.823121
2021/08/03 6021.0 0.382762 -0.307690
2021/08/04 5184.0 0.224371 -0.159779
2021/08/05 -1820.0 0.213700 0.045172
2021/08/06 2199.0 0.210503 0.053557
2021/08/07 15793.0 0.433313 1.324466
2021/08/08 -6053.0 0.227528 0.915187
2021/08/09 -22049.0 -1.476812 -2.419552
2021/08/10 4498.0 0.864029 -0.313854
2021/08/11 5951.0 -0.361891 0.011769
2021/08/12 -1827.0 -0.178059 -0.032996
2021/08/13 5810.0 0.349272 0.207221
2021/08/14 14591.0 0.313612 1.626783
2021/08/15 -7036.0 -0.030712 0.621387
2021/08/16 -18277.0 -0.492023 -0.229886
2021/08/17 1743.0 -0.320392 -2.102970
2021/08/18 10810.0 0.231022 -0.418104
2021/08/19 -3599.0 0.186924 0.148053
2021/08/20 1624.0 0.140931 0.255090
2021/08/21 24481.0 0.576043 2.264528
2021/08/22 -9835.0 -0.445505 0.305457
2021/08/23 -28924.0 -0.563361 -2.491265
2021/08/24 9165.0 0.635171 -0.388304
2021/08/25 9759.0 -0.326547 -0.162770
2021/08/26 -7134.0 0.101249 0.099862
2021/08/27 3970.0 0.335959 0.163582
2021/08/28 25874.0 -0.250823 1.778201
2021/08/29 -13585.0 0.145392 0.699918
2021/08/30 -28219.0 -0.665134 -2.349837
2021/08/31 4930.0 0.730510 -0.323811
VAR예측모델 생성
C:\Users\USER\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:162: ValueWarning: No frequency information was provided, so inferred frequency D will be used.
% freq, ValueWarning)
AIC 확인
[15.887749080562726, 15.56941157221202, 15.447484531580532, 15.353542527827047, 14.532515718515475, 13.88250706860455, 13.747371575385314, 13.740518436749893, 13.73633567278574, 13.750284608194141, 13.768015273764561, 13.754635710788872, 13.59487413296432, 13.560989601679083, 13.570246134174129, 13.583343147320788, 13.59949595591382, 13.613712798532108, 13.615760459739604, 13.575122086502608, 13.551652624220331, 13.571950947630564, 13.583125371822046, 13.595726243900693, 13.607159400774231, 13.624700389162745, 13.604028071581409, 13.602535608564912, 13.615067562544134]
최적값 확인
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Tue, 07, Sep, 2021
Time: 05:11:29
--------------------------------------------------------------------
No. of Equations: 3.00000 BIC: 14.5864
Nobs: 861.000 HQIC: 13.9623
Log likelihood: -9326.21 FPE: 786883.
AIC: 13.5751 Det(Omega_mle): 640807.
--------------------------------------------------------------------
Results for equation ticketing_count
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 180.028839 232.494775 0.774 0.439
L1.ticketing_count -0.506965 0.035674 -14.211 0.000
L1.p1 -1996.120266 876.707419 -2.277 0.023
L1.p2 -2994.421342 519.469943 -5.764 0.000
L2.ticketing_count -0.591049 0.039915 -14.808 0.000
L2.p1 -1282.433066 908.650925 -1.411 0.158
L2.p2 -632.361889 555.039082 -1.139 0.255
L3.ticketing_count -0.394801 0.044914 -8.790 0.000
L3.p1 -1196.789231 927.339100 -1.291 0.197
L3.p2 -1095.069528 590.695951 -1.854 0.064
L4.ticketing_count -0.261355 0.046867 -5.577 0.000
L4.p1 -329.295832 948.482444 -0.347 0.728
L4.p2 -1277.584888 613.594019 -2.082 0.037
L5.ticketing_count -0.331604 0.047877 -6.926 0.000
L5.p1 -1451.385110 955.325162 -1.519 0.129
L5.p2 -575.436449 638.789387 -0.901 0.368
L6.ticketing_count -0.092511 0.049281 -1.877 0.060
L6.p1 -628.070332 970.333321 -0.647 0.517
L6.p2 -1162.683493 658.884535 -1.765 0.078
L7.ticketing_count 0.161292 0.049347 3.269 0.001
L7.p1 -167.323245 968.684126 -0.173 0.863
L7.p2 -1042.236336 672.634705 -1.549 0.121
L8.ticketing_count 0.015200 0.049072 0.310 0.757
L8.p1 -177.784854 977.960074 -0.182 0.856
L8.p2 137.289055 669.284746 0.205 0.837
L9.ticketing_count -0.046776 0.048804 -0.958 0.338
L9.p1 -600.415419 977.591579 -0.614 0.539
L9.p2 -558.016498 666.026245 -0.838 0.402
L10.ticketing_count -0.101711 0.048585 -2.093 0.036
L10.p1 -287.906427 978.092089 -0.294 0.768
L10.p2 -666.837544 668.975320 -0.997 0.319
L11.ticketing_count -0.175435 0.048576 -3.612 0.000
L11.p1 -582.090970 979.222129 -0.594 0.552
L11.p2 -131.352511 669.465062 -0.196 0.844
L12.ticketing_count -0.162653 0.048951 -3.323 0.001
L12.p1 453.433169 979.924409 0.463 0.644
L12.p2 -1117.098119 669.825231 -1.668 0.095
L13.ticketing_count -0.244627 0.049291 -4.963 0.000
L13.p1 -272.802848 982.249031 -0.278 0.781
L13.p2 -143.686374 675.792744 -0.213 0.832
L14.ticketing_count 0.068766 0.049694 1.384 0.166
L14.p1 -740.111283 976.302902 -0.758 0.448
L14.p2 -428.037568 678.910815 -0.630 0.528
L15.ticketing_count -0.020000 0.049638 -0.403 0.687
L15.p1 -1169.538174 978.225955 -1.196 0.232
L15.p2 419.247354 662.784393 0.633 0.527
L16.ticketing_count -0.014447 0.048144 -0.300 0.764
L16.p1 632.082882 966.539034 0.654 0.513
L16.p2 -966.958352 643.951331 -1.502 0.133
L17.ticketing_count -0.058850 0.047314 -1.244 0.214
L17.p1 -2166.603125 951.798396 -2.276 0.023
L17.p2 -326.016777 625.640090 -0.521 0.602
L18.ticketing_count -0.124520 0.045196 -2.755 0.006
L18.p1 -427.503247 935.925604 -0.457 0.648
L18.p2 -708.220969 603.332673 -1.174 0.240
L19.ticketing_count -0.118541 0.040125 -2.954 0.003
L19.p1 -1254.316319 920.116043 -1.363 0.173
L19.p2 -1055.082102 561.550757 -1.879 0.060
L20.ticketing_count -0.157318 0.034730 -4.530 0.000
L20.p1 -374.415333 884.645882 -0.423 0.672
L20.p2 -594.394189 541.719457 -1.097 0.273
======================================================================================
Results for equation p1
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.019666 0.009854 1.996 0.046
L1.ticketing_count 0.000000 0.000002 0.314 0.754
L1.p1 -0.299508 0.037157 -8.061 0.000
L1.p2 -0.115439 0.022016 -5.243 0.000
L2.ticketing_count -0.000005 0.000002 -2.716 0.007
L2.p1 -0.208394 0.038510 -5.411 0.000
L2.p2 -0.017499 0.023524 -0.744 0.457
L3.ticketing_count -0.000003 0.000002 -1.771 0.077
L3.p1 -0.236480 0.039302 -6.017 0.000
L3.p2 -0.046923 0.025035 -1.874 0.061
L4.ticketing_count -0.000005 0.000002 -2.483 0.013
L4.p1 -0.143604 0.040199 -3.572 0.000
L4.p2 -0.069213 0.026005 -2.661 0.008
L5.ticketing_count -0.000002 0.000002 -1.079 0.280
L5.p1 -0.204063 0.040489 -5.040 0.000
L5.p2 -0.023829 0.027073 -0.880 0.379
L6.ticketing_count -0.000004 0.000002 -1.910 0.056
L6.p1 -0.108334 0.041125 -2.634 0.008
L6.p2 -0.037217 0.027925 -1.333 0.183
L7.ticketing_count -0.000003 0.000002 -1.281 0.200
L7.p1 0.158004 0.041055 3.849 0.000
L7.p2 0.013782 0.028508 0.483 0.629
L8.ticketing_count -0.000002 0.000002 -1.074 0.283
L8.p1 0.004660 0.041448 0.112 0.910
L8.p2 0.003852 0.028366 0.136 0.892
L9.ticketing_count -0.000003 0.000002 -1.607 0.108
L9.p1 -0.047877 0.041432 -1.156 0.248
L9.p2 -0.007753 0.028228 -0.275 0.784
L10.ticketing_count -0.000000 0.000002 -0.077 0.939
L10.p1 0.040333 0.041453 0.973 0.331
L10.p2 -0.030906 0.028352 -1.090 0.276
L11.ticketing_count -0.000002 0.000002 -0.810 0.418
L11.p1 0.065376 0.041501 1.575 0.115
L11.p2 -0.022272 0.028373 -0.785 0.432
L12.ticketing_count -0.000001 0.000002 -0.655 0.512
L12.p1 0.040571 0.041531 0.977 0.329
L12.p2 -0.032938 0.028389 -1.160 0.246
L13.ticketing_count -0.000000 0.000002 -0.097 0.922
L13.p1 0.028376 0.041630 0.682 0.495
L13.p2 -0.051221 0.028641 -1.788 0.074
L14.ticketing_count -0.000000 0.000002 -0.147 0.883
L14.p1 0.117161 0.041378 2.832 0.005
L14.p2 0.029178 0.028774 1.014 0.311
L15.ticketing_count 0.000002 0.000002 0.770 0.441
L15.p1 -0.016478 0.041459 -0.397 0.691
L15.p2 -0.004837 0.028090 -0.172 0.863
L16.ticketing_count 0.000002 0.000002 1.001 0.317
L16.p1 -0.033185 0.040964 -0.810 0.418
L16.p2 -0.047270 0.027292 -1.732 0.083
L17.ticketing_count 0.000001 0.000002 0.477 0.633
L17.p1 -0.075659 0.040339 -1.876 0.061
L17.p2 -0.018602 0.026516 -0.702 0.483
L18.ticketing_count 0.000000 0.000002 0.082 0.935
L18.p1 -0.098411 0.039666 -2.481 0.013
L18.p2 -0.029382 0.025570 -1.149 0.251
L19.ticketing_count -0.000001 0.000002 -0.630 0.529
L19.p1 -0.105450 0.038996 -2.704 0.007
L19.p2 -0.012101 0.023800 -0.508 0.611
L20.ticketing_count -0.000002 0.000001 -1.364 0.173
L20.p1 -0.115861 0.037493 -3.090 0.002
L20.p2 -0.018901 0.022959 -0.823 0.410
======================================================================================
Results for equation p2
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.005365 0.016665 0.322 0.747
L1.ticketing_count 0.000002 0.000003 0.759 0.448
L1.p1 -0.176178 0.062840 -2.804 0.005
L1.p2 -0.399597 0.037234 -10.732 0.000
L2.ticketing_count -0.000004 0.000003 -1.304 0.192
L2.p1 0.073331 0.065130 1.126 0.260
L2.p2 -0.428162 0.039784 -10.762 0.000
L3.ticketing_count -0.000004 0.000003 -1.121 0.262
L3.p1 -0.029908 0.066469 -0.450 0.653
L3.p2 -0.366225 0.042340 -8.650 0.000
L4.ticketing_count -0.000001 0.000003 -0.443 0.658
L4.p1 0.002232 0.067985 0.033 0.974
L4.p2 -0.390555 0.043981 -8.880 0.000
L5.ticketing_count -0.000001 0.000003 -0.241 0.810
L5.p1 -0.101401 0.068475 -1.481 0.139
L5.p2 -0.323031 0.045787 -7.055 0.000
L6.ticketing_count -0.000001 0.000004 -0.323 0.747
L6.p1 -0.042435 0.069551 -0.610 0.542
L6.p2 -0.275340 0.047227 -5.830 0.000
L7.ticketing_count 0.000000 0.000004 0.020 0.984
L7.p1 0.175276 0.069433 2.524 0.012
L7.p2 -0.029190 0.048213 -0.605 0.545
L8.ticketing_count -0.000000 0.000004 -0.141 0.888
L8.p1 -0.014910 0.070098 -0.213 0.832
L8.p2 -0.100022 0.047973 -2.085 0.037
L9.ticketing_count 0.000002 0.000003 0.612 0.540
L9.p1 0.000756 0.070071 0.011 0.991
L9.p2 -0.174628 0.047739 -3.658 0.000
L10.ticketing_count 0.000002 0.000003 0.513 0.608
L10.p1 -0.007293 0.070107 -0.104 0.917
L10.p2 -0.102901 0.047950 -2.146 0.032
L11.ticketing_count -0.000001 0.000003 -0.419 0.675
L11.p1 0.119493 0.070188 1.702 0.089
L11.p2 -0.134612 0.047986 -2.805 0.005
L12.ticketing_count -0.000001 0.000004 -0.337 0.736
L12.p1 0.040522 0.070238 0.577 0.564
L12.p2 -0.179337 0.048011 -3.735 0.000
L13.ticketing_count -0.000001 0.000004 -0.309 0.758
L13.p1 -0.113965 0.070405 -1.619 0.106
L13.p2 -0.128749 0.048439 -2.658 0.008
L14.ticketing_count 0.000000 0.000004 0.006 0.995
L14.p1 0.120356 0.069979 1.720 0.085
L14.p2 0.039364 0.048663 0.809 0.419
L15.ticketing_count -0.000001 0.000004 -0.392 0.695
L15.p1 -0.047995 0.070117 -0.685 0.494
L15.p2 -0.028690 0.047507 -0.604 0.546
L16.ticketing_count 0.000000 0.000003 0.075 0.940
L16.p1 -0.012248 0.069279 -0.177 0.860
L16.p2 -0.166658 0.046157 -3.611 0.000
L17.ticketing_count 0.000001 0.000003 0.153 0.878
L17.p1 -0.148408 0.068222 -2.175 0.030
L17.p2 -0.111562 0.044844 -2.488 0.013
L18.ticketing_count 0.000000 0.000003 0.094 0.925
L18.p1 -0.089924 0.067085 -1.340 0.180
L18.p2 -0.144690 0.043245 -3.346 0.001
L19.ticketing_count -0.000001 0.000003 -0.482 0.630
L19.p1 -0.075462 0.065952 -1.144 0.253
L19.p2 -0.156261 0.040251 -3.882 0.000
L20.ticketing_count 0.000001 0.000002 0.259 0.796
L20.p1 -0.156387 0.063409 -2.466 0.014
L20.p2 -0.105156 0.038829 -2.708 0.007
======================================================================================
Correlation matrix of residuals
ticketing_count p1 p2
ticketing_count 1.000000 0.155754 0.182997
p1 0.155754 1.000000 0.305798
p2 0.182997 0.305798 1.000000
입력한92일 차분 예측
입력한92일 차분을 더해서 원래값 예측
ticketing_count p1 p2 ticketing_count_forecasted \
date
2021/06/01 10738.225435 0.003609 -0.181806 14512.225435
2021/06/02 8272.966484 0.335877 0.610832 22785.191919
2021/06/03 -5479.580407 -0.098172 -0.418347 17305.611512
2021/06/04 1976.071958 -0.020856 -0.123805 19281.683471
2021/06/05 26484.892618 0.429143 1.336143 45766.576089
2021/06/06 -15567.376928 0.296994 0.578678 30199.199160
2021/06/07 -26476.727310 -0.749423 -1.750186 3722.471850
2021/06/08 9794.489518 0.033773 -0.087741 13516.961368
2021/06/09 10003.605273 0.254765 0.570758 23520.566641
2021/06/10 -7649.214317 -0.076366 -0.357663 15871.352323
2021/06/11 4737.019765 0.019785 0.014513 20608.372088
2021/06/12 22556.616097 0.333786 1.046902 43164.988185
2021/06/13 -12497.476873 0.222930 0.430612 30667.511312
2021/06/14 -27523.550912 -0.800864 -1.675935 3143.960400
2021/06/15 10685.501455 0.008486 -0.187669 13829.461856
2021/06/16 8459.611554 0.287550 0.562890 22289.073410
2021/06/17 -6097.383683 -0.042078 -0.265213 16191.689726
2021/06/18 3830.848397 0.040522 0.013506 20022.538124
2021/06/19 23216.635344 0.412503 1.115534 43239.173468
2021/06/20 -12949.970584 0.200237 0.419620 30289.202883
2021/06/21 -26346.463297 -0.780807 -1.572016 3942.739586
2021/06/22 9900.652911 -0.028379 -0.204454 13843.392497
2021/06/23 9418.933605 0.253237 0.515727 23262.326102
2021/06/24 -7244.168588 -0.081135 -0.295158 16018.157514
2021/06/25 4114.684594 0.019383 -0.031270 20132.842108
2021/06/26 22520.506107 0.427033 1.072264 42653.348216
2021/06/27 -11948.740037 0.206255 0.434432 30704.608179
2021/06/28 -26369.050329 -0.738613 -1.489838 4335.557849
2021/06/29 10025.095468 -0.024260 -0.175833 14360.653318
2021/06/30 9121.782048 0.262215 0.507920 23482.435366
2021/07/01 -7062.601179 -0.077727 -0.247359 16419.834187
2021/07/02 4222.259606 0.026796 -0.027605 20642.093793
2021/07/03 21676.687141 0.425464 1.039223 42318.780934
2021/07/04 -11643.511226 0.185593 0.385975 30675.269709
2021/07/05 -25809.163856 -0.731623 -1.454982 4866.105853
2021/07/06 9519.367540 -0.043087 -0.215308 14385.473393
2021/07/07 9202.855439 0.250935 0.488466 23588.328832
2021/07/08 -7077.173240 -0.073766 -0.238705 16511.155592
2021/07/09 4229.318615 0.030127 -0.021233 20740.474207
2021/07/10 21163.155562 0.441500 1.038095 41903.629769
2021/07/11 -11245.580736 0.183583 0.390042 30658.049033
2021/07/12 -25246.380737 -0.699911 -1.393116 5411.668296
2021/07/13 9104.203797 -0.053388 -0.223275 14515.872093
2021/07/14 9275.966984 0.244034 0.466770 23791.839077
2021/07/15 -7198.016622 -0.081335 -0.245606 16593.822455
2021/07/16 4271.744895 0.021325 -0.037177 20865.567350
2021/07/17 20501.461571 0.435268 1.010610 41367.028921
2021/07/18 -10768.290659 0.174439 0.374575 30598.738262
2021/07/19 -24754.806081 -0.675964 -1.341104 5843.932181
2021/07/20 8776.490705 -0.058631 -0.226528 14620.422886
2021/07/21 9260.471006 0.243466 0.463311 23880.893892
2021/07/22 -7073.330982 -0.077082 -0.230704 16807.562910
2021/07/23 4262.589721 0.022348 -0.033417 21070.152631
2021/07/24 19988.376734 0.434473 0.996830 41058.529365
2021/07/25 -10478.903077 0.166010 0.360778 30579.626287
2021/07/26 -24188.928250 -0.655360 -1.301500 6390.698037
2021/07/27 8324.585279 -0.069618 -0.242901 14715.283316
2021/07/28 9272.885480 0.236394 0.446232 23988.168797
2021/07/29 -7041.835025 -0.077927 -0.230495 16946.333772
2021/07/30 4274.862466 0.019763 -0.035025 21221.196237
2021/07/31 19455.314534 0.432609 0.984250 40676.510771
2021/08/01 -10113.680681 0.161105 0.356362 30562.830091
2021/08/02 -23645.012785 -0.630523 -1.253272 6917.817306
2021/08/03 7950.453167 -0.074994 -0.247440 14868.270473
2021/08/04 9234.835311 0.233218 0.435573 24103.105784
2021/08/05 -6960.708057 -0.077296 -0.228926 17142.397727
2021/08/06 4232.042110 0.016761 -0.040356 21374.439837
2021/08/07 18949.873023 0.426759 0.964658 40324.312860
2021/08/08 -9807.658259 0.153877 0.344480 30516.654601
2021/08/09 -23108.429677 -0.610128 -1.213529 7408.224924
2021/08/10 7565.595084 -0.081201 -0.254337 14973.820007
2021/08/11 9194.282417 0.229482 0.426791 24168.102424
2021/08/12 -6853.034486 -0.075063 -0.223570 17315.067938
2021/08/13 4206.293604 0.015914 -0.039392 21521.361542
2021/08/14 18484.659841 0.422922 0.950480 40006.021383
2021/08/15 -9508.716658 0.148672 0.337053 30497.304725
2021/08/16 -22578.589188 -0.589647 -1.174873 7918.715536
2021/08/17 7189.395418 -0.087063 -0.261699 15108.110954
2021/08/18 9129.420549 0.224775 0.414652 24237.531503
2021/08/19 -6750.738151 -0.074266 -0.222231 17486.793352
2021/08/20 4163.987979 0.013748 -0.041944 21650.781331
2021/08/21 18029.900174 0.417437 0.934285 39680.681505
2021/08/22 -9214.123481 0.143652 0.330202 30466.558024
2021/08/23 -22057.270585 -0.569830 -1.136139 8409.287439
2021/08/24 6842.063798 -0.091324 -0.265397 15251.351237
2021/08/25 9052.010363 0.221048 0.405612 24303.361601
2021/08/26 -6628.683212 -0.072392 -0.218791 17674.678388
2021/08/27 4111.739041 0.012267 -0.043633 21786.417429
2021/08/28 17595.798385 0.411670 0.917228 39382.215814
2021/08/29 -8939.504093 0.138667 0.322110 30442.711721
2021/08/30 -21551.352960 -0.551612 -1.101251 8891.358761
2021/08/31 6499.971448 -0.095724 -0.270115 15391.330209
p1_forecasted p2_forecasted
date
2021/06/01 3.528737 -1.329321
2021/06/02 3.864613 -0.718489
2021/06/03 3.766441 -1.136836
2021/06/04 3.745586 -1.260641
2021/06/05 4.174729 0.075502
2021/06/06 4.471723 0.654180
2021/06/07 3.722300 -1.096006
2021/06/08 3.756073 -1.183747
2021/06/09 4.010837 -0.612989
2021/06/10 3.934472 -0.970651
2021/06/11 3.954257 -0.956138
2021/06/12 4.288043 0.090765
2021/06/13 4.510973 0.521376
2021/06/14 3.710109 -1.154558
2021/06/15 3.718596 -1.342227
2021/06/16 4.006145 -0.779338
2021/06/17 3.964068 -1.044550
2021/06/18 4.004590 -1.031045
2021/06/19 4.417093 0.084490
2021/06/20 4.617329 0.504109
2021/06/21 3.836522 -1.067907
2021/06/22 3.808143 -1.272360
2021/06/23 4.061380 -0.756633
2021/06/24 3.980246 -1.051792
2021/06/25 3.999629 -1.083061
2021/06/26 4.426662 -0.010797
2021/06/27 4.632916 0.423635
2021/06/28 3.894303 -1.066204
2021/06/29 3.870043 -1.242037
2021/06/30 4.132259 -0.734117
2021/07/01 4.054531 -0.981476
2021/07/02 4.081327 -1.009081
2021/07/03 4.506791 0.030142
2021/07/04 4.692384 0.416117
2021/07/05 3.960762 -1.038865
2021/07/06 3.917674 -1.254174
2021/07/07 4.168610 -0.765707
2021/07/08 4.094844 -1.004412
2021/07/09 4.124971 -1.025645
2021/07/10 4.566471 0.012449
2021/07/11 4.750054 0.402491
2021/07/12 4.050143 -0.990625
2021/07/13 3.996755 -1.213900
2021/07/14 4.240789 -0.747130
2021/07/15 4.159454 -0.992736
2021/07/16 4.180779 -1.029913
2021/07/17 4.616047 -0.019303
2021/07/18 4.790485 0.355272
2021/07/19 4.114521 -0.985832
2021/07/20 4.055891 -1.212360
2021/07/21 4.299356 -0.749049
2021/07/22 4.222274 -0.979753
2021/07/23 4.244623 -1.013170
2021/07/24 4.679095 -0.016340
2021/07/25 4.845105 0.344438
2021/07/26 4.189745 -0.957062
2021/07/27 4.120127 -1.199963
2021/07/28 4.356522 -0.753731
2021/07/29 4.278595 -0.984226
2021/07/30 4.298357 -1.019251
2021/07/31 4.730967 -0.035001
2021/08/01 4.892072 0.321360
2021/08/02 4.261548 -0.931912
2021/08/03 4.186554 -1.179352
2021/08/04 4.419772 -0.743778
2021/08/05 4.342476 -0.972704
2021/08/06 4.359237 -1.013060
2021/08/07 4.785996 -0.048402
2021/08/08 4.939873 0.296078
2021/08/09 4.329745 -0.917450
2021/08/10 4.248544 -1.171787
2021/08/11 4.478026 -0.744996
2021/08/12 4.402963 -0.968566
2021/08/13 4.418877 -1.007957
2021/08/14 4.841799 -0.057477
2021/08/15 4.990471 0.279576
2021/08/16 4.400823 -0.895298
2021/08/17 4.313760 -1.156997
2021/08/18 4.538535 -0.742345
2021/08/19 4.464269 -0.964576
2021/08/20 4.478017 -1.006520
2021/08/21 4.895454 -0.072235
2021/08/22 5.039106 0.257967
2021/08/23 4.469276 -0.878172
2021/08/24 4.377951 -1.143569
2021/08/25 4.598999 -0.737957
2021/08/26 4.526608 -0.956748
2021/08/27 4.538875 -1.000381
2021/08/28 4.950545 -0.083153
2021/08/29 5.089213 0.238957
2021/08/30 4.537601 -0.862294
2021/08/31 4.441877 -1.132409
MSE: 25884948.33749545
RMSE: 5087.725261597313
Variance score: 0.809
#모델을 이용하여 앞으로의 예측을 하는 함수 선언(테스트데이터 없음)(진짜 앞으로의 미래 데이터 예측)
#변수로 전처리 및 PCA가 완료된 데이터프레임(df) 및 예측 및 평가를 원하는 일 수(day)를 입력
def get_predict(df, day):
print("차분 전 정상성 평가")
for i in df.columns:
adfuller_test = adfuller(df[i],autolag='AIC')
print(i)
print("ADF test statistic: {}".format(adfuller_test[0]))
print("p-value: {}".format(adfuller_test[1]))
df_diff = df.diff().dropna()
print("차분 플롯")
df_diff.plot(figsize=(20,20))
print("차분")
print(df_diff)
print("차분 후 정상성 평가")
for i in df.columns:
adfuller_test = adfuller(df_diff[i],autolag='AIC')
print(i)
print("ADF test statistic: {}".format(adfuller_test[0]))
print("p-value: {}".format(adfuller_test[1]))
print("학습 데이터 생성 및 예측데이터 담을 인덱스생성")
train = df_diff
print(train)
predict_date = pd.date_range("2021/09/01", periods=day)
predict_date = predict_date.astype(str)
predict_date = predict_date.str.split("-").str[0]+"/"+predict_date.str.split("-").str[1]+\
"/"+predict_date.str.split("-").str[2]
print("VAR예측모델 생성")
forecasting_model = VAR(train)
results_aic = []
for p in range(1,50):
results = forecasting_model.fit(p)
results_aic.append(results.aic)
print("AIC 확인")
sns.set()
plt.plot(list(np.arange(1,50,1)), results_aic)
plt.xlabel("Order")
plt.ylabel("AIC")
plt.show()
print(results_aic)
print("최적값 확인")
results = forecasting_model.fit(np.argsort(results_aic)[0])
results.summary()
print(f"입력한{day}일 차분 예측")
laaged_values = train.values
forecast = pd.DataFrame(results.forecast(y= laaged_values, steps=day),
index = predict_date,
columns=df.columns)
print(f"입력한{day}일 차분을 더해서 원래값 예측")
for i in df.columns:
forecast[f'{i}_forecasted']= df[i].iloc[-1]+forecast[i].cumsum()
print("예측값")
print(forecast)
forecast['ticketing_count_forecasted'].plot(figsize=(20,15))
return forecast#예매 건수를 정수로 만든 후 저장
#1달치
forecast_ = get_predict(df,30)
forecast_.iloc[:,3:]
forecast_ = forecast_['ticketing_count_forecasted'].round().astype(int)
forecast_.to_csv(f"F:\\drive\\WebWorkPlace2021\\jupyter\\code\\예매건수실전예측(1달).csv")차분 전 정상성 평가
ticketing_count
ADF test statistic: -2.099553733500803
p-value: 0.24469408536639126
p1
ADF test statistic: -0.21267008715649313
p-value: 0.9369944290578626
p2
ADF test statistic: -0.9660338259001354
p-value: 0.7654502757577035
차분 플롯
차분
ticketing_count p1 p2
date
2019/01/02 -2332.0 -0.591827 -1.436540
2019/01/03 1429.0 0.050071 0.043894
2019/01/04 590.0 0.032127 -0.160157
2019/01/05 11667.0 0.453149 0.965645
2019/01/06 -5564.0 0.215044 0.535168
... ... ... ...
2021/08/27 3970.0 0.335959 0.163582
2021/08/28 25874.0 -0.250823 1.778201
2021/08/29 -13585.0 0.145392 0.699918
2021/08/30 -28219.0 -0.665134 -2.349837
2021/08/31 4930.0 0.730510 -0.323811
[973 rows x 3 columns]
차분 후 정상성 평가
ticketing_count
ADF test statistic: -8.90366524755091
p-value: 1.1532103667357817e-14
p1
ADF test statistic: -7.316333641273258
p-value: 1.2265309593054393e-10
p2
ADF test statistic: -8.654863167499396
p-value: 5.0006128672818535e-14
학습 데이터 생성 및 예측데이터 담을 인덱스생성
ticketing_count p1 p2
date
2019/01/02 -2332.0 -0.591827 -1.436540
2019/01/03 1429.0 0.050071 0.043894
2019/01/04 590.0 0.032127 -0.160157
2019/01/05 11667.0 0.453149 0.965645
2019/01/06 -5564.0 0.215044 0.535168
... ... ... ...
2021/08/27 3970.0 0.335959 0.163582
2021/08/28 25874.0 -0.250823 1.778201
2021/08/29 -13585.0 0.145392 0.699918
2021/08/30 -28219.0 -0.665134 -2.349837
2021/08/31 4930.0 0.730510 -0.323811
[973 rows x 3 columns]
VAR예측모델 생성
C:\Users\USER\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:162: ValueWarning: No frequency information was provided, so inferred frequency D will be used.
% freq, ValueWarning)
AIC 확인
[15.90069559663783, 15.572796595323888, 15.438512269362898, 15.31754572453941, 14.474261894433562, 13.799665066727854, 13.669086564814712, 13.65876525492896, 13.656428475925214, 13.668539921532226, 13.681190675487326, 13.664512252200915, 13.506288204417192, 13.469633668260517, 13.479833314212462, 13.491712686201456, 13.507522473115344, 13.517533721136573, 13.514262134979266, 13.469163895291477, 13.443022659364134, 13.460504604688218, 13.473040467677867, 13.484688931315802, 13.495399070349762, 13.510175068018649, 13.49175709209404, 13.491702356470975, 13.501593114985997, 13.504594156560776, 13.505911140893375, 13.501083244172221, 13.49502634071845, 13.4825402365553, 13.485702287271796, 13.491493702045586, 13.501128156317233, 13.512071363254185, 13.51453437071208, 13.519701052802892, 13.520395791839347, 13.51838091174, 13.529890257546343, 13.54548322923905, 13.557810944948548, 13.577223037311466, 13.585091312860753, 13.584819969570573, 13.587481853395822]
최적값 확인
입력한30일 차분 예측
입력한30일 차분을 더해서 원래값 예측
예측값
ticketing_count p1 p2 ticketing_count_forecasted \
2021/09/01 8379.822447 -0.229706 -0.268557 16961.822447
2021/09/02 -3013.063096 0.169348 0.169535 13948.759351
2021/09/03 4792.668778 0.095916 0.373861 18741.428129
2021/09/04 20225.006527 0.386125 1.704444 38966.434656
2021/09/05 -10136.242784 -0.067023 0.371076 28830.191872
2021/09/06 -23969.370043 -0.595097 -1.715073 4860.821829
2021/09/07 2207.221154 0.116867 -0.389266 7068.042983
2021/09/08 9997.596857 -0.111956 -0.309473 17065.639841
2021/09/09 -3103.025116 0.169983 -0.047908 13962.614725
2021/09/10 3497.017794 0.133294 0.476790 17459.632518
2021/09/11 23318.455698 0.392189 1.695311 40778.088216
2021/09/12 -9992.588391 0.096094 0.364947 30785.499825
2021/09/13 -27126.774124 -0.756169 -1.930844 3658.725702
2021/09/14 5402.260793 0.162654 -0.177296 9060.986495
2021/09/15 7980.389948 -0.093019 -0.274704 17041.376443
2021/09/16 -2147.577998 0.162500 -0.038151 14893.798445
2021/09/17 3349.624443 0.125116 0.423708 18243.422888
2021/09/18 22830.461262 0.361325 1.516753 41073.884149
2021/09/19 -10955.898071 0.052654 0.258291 30117.986078
2021/09/20 -24617.505615 -0.769187 -1.710191 5500.480463
2021/09/21 3368.053573 0.093730 -0.261761 8868.534036
2021/09/22 8424.405369 -0.034282 -0.237434 17292.939405
2021/09/23 -2691.801564 0.160833 -0.045882 14601.137841
2021/09/24 4468.807709 0.125172 0.420745 19069.945550
2021/09/25 21571.036752 0.455940 1.509045 40640.982302
2021/09/26 -10743.534198 0.061135 0.244850 29897.448104
2021/09/27 -24241.424586 -0.733965 -1.600000 5656.023518
2021/09/28 3576.987366 0.022834 -0.269996 9233.010884
2021/09/29 7361.993877 -0.023275 -0.231131 16595.004761
2021/09/30 -2069.494762 0.124274 -0.086842 14525.509999
p1_forecasted p2_forecasted
2021/09/01 4.847317 -3.658249
2021/09/02 5.016665 -3.488714
2021/09/03 5.112581 -3.114854
2021/09/04 5.498707 -1.410409
2021/09/05 5.431684 -1.039333
2021/09/06 4.836586 -2.754407
2021/09/07 4.953453 -3.143673
2021/09/08 4.841497 -3.453146
2021/09/09 5.011480 -3.501054
2021/09/10 5.144774 -3.024264
2021/09/11 5.536963 -1.328953
2021/09/12 5.633057 -0.964006
2021/09/13 4.876888 -2.894850
2021/09/14 5.039541 -3.072146
2021/09/15 4.946523 -3.346850
2021/09/16 5.109023 -3.385002
2021/09/17 5.234139 -2.961294
2021/09/18 5.595463 -1.444541
2021/09/19 5.648117 -1.186250
2021/09/20 4.878930 -2.896441
2021/09/21 4.972660 -3.158202
2021/09/22 4.938378 -3.395635
2021/09/23 5.099211 -3.441517
2021/09/24 5.224383 -3.020772
2021/09/25 5.680323 -1.511727
2021/09/26 5.741458 -1.266877
2021/09/27 5.007493 -2.866877
2021/09/28 5.030328 -3.136873
2021/09/29 5.007053 -3.368004
2021/09/30 5.131327 -3.454846 
#예매 건수를 정수로 만든 후 저장
#3달치
forecast_ = get_predict(df,92)
forecast_.iloc[:,3:]
forecast_ = forecast_['ticketing_count_forecasted'].round().astype(int)
forecast_.to_csv(f"F:\\drive\\WebWorkPlace2021\\jupyter\\code\\예매건수실전예측(3달).csv")차분 전 정상성 평가
ticketing_count
ADF test statistic: -2.099553733500803
p-value: 0.24469408536639126
p1
ADF test statistic: -0.21267008715649313
p-value: 0.9369944290578626
p2
ADF test statistic: -0.9660338259001354
p-value: 0.7654502757577035
차분 플롯
차분
ticketing_count p1 p2
date
2019/01/02 -2332.0 -0.591827 -1.436540
2019/01/03 1429.0 0.050071 0.043894
2019/01/04 590.0 0.032127 -0.160157
2019/01/05 11667.0 0.453149 0.965645
2019/01/06 -5564.0 0.215044 0.535168
... ... ... ...
2021/08/27 3970.0 0.335959 0.163582
2021/08/28 25874.0 -0.250823 1.778201
2021/08/29 -13585.0 0.145392 0.699918
2021/08/30 -28219.0 -0.665134 -2.349837
2021/08/31 4930.0 0.730510 -0.323811
[973 rows x 3 columns]
차분 후 정상성 평가
ticketing_count
ADF test statistic: -8.90366524755091
p-value: 1.1532103667357817e-14
p1
ADF test statistic: -7.316333641273258
p-value: 1.2265309593054393e-10
p2
ADF test statistic: -8.654863167499396
p-value: 5.0006128672818535e-14
학습 데이터 생성 및 예측데이터 담을 인덱스생성
ticketing_count p1 p2
date
2019/01/02 -2332.0 -0.591827 -1.436540
2019/01/03 1429.0 0.050071 0.043894
2019/01/04 590.0 0.032127 -0.160157
2019/01/05 11667.0 0.453149 0.965645
2019/01/06 -5564.0 0.215044 0.535168
... ... ... ...
2021/08/27 3970.0 0.335959 0.163582
2021/08/28 25874.0 -0.250823 1.778201
2021/08/29 -13585.0 0.145392 0.699918
2021/08/30 -28219.0 -0.665134 -2.349837
2021/08/31 4930.0 0.730510 -0.323811
[973 rows x 3 columns]
VAR예측모델 생성
C:\Users\USER\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:162: ValueWarning: No frequency information was provided, so inferred frequency D will be used.
% freq, ValueWarning)
AIC 확인
[15.90069559663783, 15.572796595323888, 15.438512269362898, 15.31754572453941, 14.474261894433562, 13.799665066727854, 13.669086564814712, 13.65876525492896, 13.656428475925214, 13.668539921532226, 13.681190675487326, 13.664512252200915, 13.506288204417192, 13.469633668260517, 13.479833314212462, 13.491712686201456, 13.507522473115344, 13.517533721136573, 13.514262134979266, 13.469163895291477, 13.443022659364134, 13.460504604688218, 13.473040467677867, 13.484688931315802, 13.495399070349762, 13.510175068018649, 13.49175709209404, 13.491702356470975, 13.501593114985997, 13.504594156560776, 13.505911140893375, 13.501083244172221, 13.49502634071845, 13.4825402365553, 13.485702287271796, 13.491493702045586, 13.501128156317233, 13.512071363254185, 13.51453437071208, 13.519701052802892, 13.520395791839347, 13.51838091174, 13.529890257546343, 13.54548322923905, 13.557810944948548, 13.577223037311466, 13.585091312860753, 13.584819969570573, 13.587481853395822]
최적값 확인
입력한92일 차분 예측
입력한92일 차분을 더해서 원래값 예측
예측값
ticketing_count p1 p2 ticketing_count_forecasted \
2021/09/01 8379.822447 -0.229706 -0.268557 16961.822447
2021/09/02 -3013.063096 0.169348 0.169535 13948.759351
2021/09/03 4792.668778 0.095916 0.373861 18741.428129
2021/09/04 20225.006527 0.386125 1.704444 38966.434656
2021/09/05 -10136.242784 -0.067023 0.371076 28830.191872
2021/09/06 -23969.370043 -0.595097 -1.715073 4860.821829
2021/09/07 2207.221154 0.116867 -0.389266 7068.042983
2021/09/08 9997.596857 -0.111956 -0.309473 17065.639841
2021/09/09 -3103.025116 0.169983 -0.047908 13962.614725
2021/09/10 3497.017794 0.133294 0.476790 17459.632518
2021/09/11 23318.455698 0.392189 1.695311 40778.088216
2021/09/12 -9992.588391 0.096094 0.364947 30785.499825
2021/09/13 -27126.774124 -0.756169 -1.930844 3658.725702
2021/09/14 5402.260793 0.162654 -0.177296 9060.986495
2021/09/15 7980.389948 -0.093019 -0.274704 17041.376443
2021/09/16 -2147.577998 0.162500 -0.038151 14893.798445
2021/09/17 3349.624443 0.125116 0.423708 18243.422888
2021/09/18 22830.461262 0.361325 1.516753 41073.884149
2021/09/19 -10955.898071 0.052654 0.258291 30117.986078
2021/09/20 -24617.505615 -0.769187 -1.710191 5500.480463
2021/09/21 3368.053573 0.093730 -0.261761 8868.534036
2021/09/22 8424.405369 -0.034282 -0.237434 17292.939405
2021/09/23 -2691.801564 0.160833 -0.045882 14601.137841
2021/09/24 4468.807709 0.125172 0.420745 19069.945550
2021/09/25 21571.036752 0.455940 1.509045 40640.982302
2021/09/26 -10743.534198 0.061135 0.244850 29897.448104
2021/09/27 -24241.424586 -0.733965 -1.600000 5656.023518
2021/09/28 3576.987366 0.022834 -0.269996 9233.010884
2021/09/29 7361.993877 -0.023275 -0.231131 16595.004761
2021/09/30 -2069.494762 0.124274 -0.086842 14525.509999
2021/10/01 4572.594305 0.130574 0.416923 19098.104303
2021/10/02 21339.093057 0.455097 1.450591 40437.197360
2021/10/03 -10720.175689 0.091146 0.258583 29717.021672
2021/10/04 -23775.400664 -0.729358 -1.526510 5941.621008
2021/10/05 3501.102912 0.012869 -0.276418 9442.723920
2021/10/06 7173.454032 -0.005277 -0.190599 16616.177951
2021/10/07 -1947.297792 0.122153 -0.077097 14668.880159
2021/10/08 5035.327328 0.131481 0.392593 19704.207487
2021/10/09 20553.235877 0.467202 1.392806 40257.443364
2021/10/10 -10834.257426 0.076547 0.240499 29423.185938
2021/10/11 -22969.411283 -0.714104 -1.458009 6453.774655
2021/10/12 3168.146878 -0.025118 -0.315309 9621.921533
2021/10/13 6893.794228 0.003841 -0.174083 16515.715762
2021/10/14 -1901.650353 0.101635 -0.089616 14614.065408
2021/10/15 5543.145464 0.134759 0.379056 20157.210873
2021/10/16 19855.822962 0.476856 1.358173 40013.033834
2021/10/17 -10689.890565 0.082074 0.252778 29323.143269
2021/10/18 -22491.905380 -0.687695 -1.390863 6831.237889
2021/10/19 3069.913079 -0.042919 -0.334210 9901.150968
2021/10/20 6463.356482 0.009572 -0.159292 16364.507451
2021/10/21 -1737.664753 0.085556 -0.102038 14626.842698
2021/10/22 5840.872891 0.132690 0.356385 20467.715588
2021/10/23 19257.049654 0.472577 1.314526 39724.765242
2021/10/24 -10628.591389 0.080870 0.257265 29096.173853
2021/10/25 -21914.112886 -0.667154 -1.332796 7182.060967
2021/10/26 2829.835764 -0.057478 -0.350505 10011.896732
2021/10/27 6296.239924 0.017496 -0.136921 16308.136655
2021/10/28 -1650.231408 0.077242 -0.102010 14657.905247
2021/10/29 6204.642371 0.135877 0.344514 20862.547618
2021/10/30 18621.592595 0.473049 1.280836 39484.140214
2021/10/31 -10489.812860 0.079202 0.261551 28994.327354
2021/11/01 -21421.572569 -0.644187 -1.281574 7572.754784
2021/11/02 2625.053683 -0.073407 -0.370973 10197.808467
2021/11/03 6059.448683 0.021407 -0.125962 16257.257151
2021/11/04 -1571.490431 0.064985 -0.110383 14685.766719
2021/11/05 6481.787052 0.135721 0.330192 21167.553772
2021/11/06 18061.173517 0.468901 1.248786 39228.727289
2021/11/07 -10322.311122 0.079095 0.268770 28906.416168
2021/11/08 -20942.333991 -0.621135 -1.230445 7964.082176
2021/11/09 2418.838618 -0.083494 -0.383234 10382.920795
2021/11/10 5862.048042 0.026044 -0.113691 16244.968837
2021/11/11 -1499.611080 0.056617 -0.116237 14745.357757
2021/11/12 6705.488707 0.135636 0.316222 21450.846464
2021/11/13 17520.063499 0.463062 1.215423 38970.909964
2021/11/14 -10153.340861 0.076888 0.270883 28817.569103
2021/11/15 -20493.446324 -0.600556 -1.186081 8324.122779
2021/11/16 2195.004980 -0.094204 -0.395681 10519.127759
2021/11/17 5702.602798 0.029444 -0.103217 16221.730558
2021/11/18 -1434.582380 0.049162 -0.120023 14787.148178
2021/11/19 6907.817891 0.136226 0.306758 21694.966069
2021/11/20 17017.254548 0.457738 1.186983 38712.220617
2021/11/21 -9951.741459 0.075976 0.274589 28760.479158
2021/11/22 -20066.967115 -0.579754 -1.143143 8693.512043
2021/11/23 1982.177266 -0.102993 -0.405984 10675.689309
2021/11/24 5543.628551 0.032150 -0.095890 16219.317860
2021/11/25 -1375.853904 0.042150 -0.125312 14843.463956
2021/11/26 7059.095358 0.135738 0.296523 21902.559315
2021/11/27 16549.245944 0.450951 1.158437 38451.805259
2021/11/28 -9742.189888 0.074590 0.276957 28709.615371
2021/11/29 -19652.604576 -0.560257 -1.102391 9057.010795
2021/11/30 1771.321672 -0.110460 -0.413296 10828.332467
2021/12/01 5405.398584 0.034720 -0.088540 16233.731050
p1_forecasted p2_forecasted
2021/09/01 4.847317 -3.658249
2021/09/02 5.016665 -3.488714
2021/09/03 5.112581 -3.114854
2021/09/04 5.498707 -1.410409
2021/09/05 5.431684 -1.039333
2021/09/06 4.836586 -2.754407
2021/09/07 4.953453 -3.143673
2021/09/08 4.841497 -3.453146
2021/09/09 5.011480 -3.501054
2021/09/10 5.144774 -3.024264
2021/09/11 5.536963 -1.328953
2021/09/12 5.633057 -0.964006
2021/09/13 4.876888 -2.894850
2021/09/14 5.039541 -3.072146
2021/09/15 4.946523 -3.346850
2021/09/16 5.109023 -3.385002
2021/09/17 5.234139 -2.961294
2021/09/18 5.595463 -1.444541
2021/09/19 5.648117 -1.186250
2021/09/20 4.878930 -2.896441
2021/09/21 4.972660 -3.158202
2021/09/22 4.938378 -3.395635
2021/09/23 5.099211 -3.441517
2021/09/24 5.224383 -3.020772
2021/09/25 5.680323 -1.511727
2021/09/26 5.741458 -1.266877
2021/09/27 5.007493 -2.866877
2021/09/28 5.030328 -3.136873
2021/09/29 5.007053 -3.368004
2021/09/30 5.131327 -3.454846
2021/10/01 5.261901 -3.037923
2021/10/02 5.716997 -1.587332
2021/10/03 5.808143 -1.328749
2021/10/04 5.078785 -2.855259
2021/10/05 5.091655 -3.131677
2021/10/06 5.086377 -3.322276
2021/10/07 5.208530 -3.399374
2021/10/08 5.340010 -3.006781
2021/10/09 5.807212 -1.613975
2021/10/10 5.883759 -1.373475
2021/10/11 5.169655 -2.831484
2021/10/12 5.144538 -3.146793
2021/10/13 5.148378 -3.320876
2021/10/14 5.250013 -3.410492
2021/10/15 5.384772 -3.031435
2021/10/16 5.861629 -1.673262
2021/10/17 5.943702 -1.420484
2021/10/18 5.256007 -2.811347
2021/10/19 5.213087 -3.145557
2021/10/20 5.222659 -3.304849
2021/10/21 5.308215 -3.406887
2021/10/22 5.440905 -3.050502
2021/10/23 5.913482 -1.735976
2021/10/24 5.994352 -1.478710
2021/10/25 5.327198 -2.811506
2021/10/26 5.269720 -3.162011
2021/10/27 5.287216 -3.298933
2021/10/28 5.364458 -3.400943
2021/10/29 5.500334 -3.056428
2021/10/30 5.973384 -1.775592
2021/10/31 6.052586 -1.514041
2021/11/01 5.408399 -2.795615
2021/11/02 5.334992 -3.166588
2021/11/03 5.356399 -3.292550
2021/11/04 5.421385 -3.402933
2021/11/05 5.557106 -3.072741
2021/11/06 6.026006 -1.823955
2021/11/07 6.105102 -1.555185
2021/11/08 5.483966 -2.785630
2021/11/09 5.400472 -3.168864
2021/11/10 5.426516 -3.282555
2021/11/11 5.483133 -3.398792
2021/11/12 5.618770 -3.082571
2021/11/13 6.081832 -1.867147
2021/11/14 6.158719 -1.596264
2021/11/15 5.558163 -2.782345
2021/11/16 5.463959 -3.178026
2021/11/17 5.493403 -3.281243
2021/11/18 5.542564 -3.401266
2021/11/19 5.678790 -3.094508
2021/11/20 6.136528 -1.907525
2021/11/21 6.212504 -1.632936
2021/11/22 5.632750 -2.776078
2021/11/23 5.529758 -3.182062
2021/11/24 5.561907 -3.277952
2021/11/25 5.604057 -3.403264
2021/11/26 5.739795 -3.106742
2021/11/27 6.190746 -1.948305
2021/11/28 6.265337 -1.671348
2021/11/29 5.705080 -2.773739
2021/11/30 5.594620 -3.187035
2021/12/01 5.629341 -3.275575 
#예매 건수를 정수로 만든 후 저장
#5달치
forecast_ = get_predict(df,153)
forecast_.iloc[:,3:]
forecast_ = forecast_['ticketing_count_forecasted'].round().astype(int)
forecast_.to_csv(f"F:\\drive\\WebWorkPlace2021\\jupyter\\code\\예매건수실전예측(5달).csv")차분 전 정상성 평가
ticketing_count
ADF test statistic: -2.099553733500803
p-value: 0.24469408536639126
p1
ADF test statistic: -0.21267008715649313
p-value: 0.9369944290578626
p2
ADF test statistic: -0.9660338259001354
p-value: 0.7654502757577035
차분 플롯
차분
ticketing_count p1 p2
date
2019/01/02 -2332.0 -0.591827 -1.436540
2019/01/03 1429.0 0.050071 0.043894
2019/01/04 590.0 0.032127 -0.160157
2019/01/05 11667.0 0.453149 0.965645
2019/01/06 -5564.0 0.215044 0.535168
... ... ... ...
2021/08/27 3970.0 0.335959 0.163582
2021/08/28 25874.0 -0.250823 1.778201
2021/08/29 -13585.0 0.145392 0.699918
2021/08/30 -28219.0 -0.665134 -2.349837
2021/08/31 4930.0 0.730510 -0.323811
[973 rows x 3 columns]
차분 후 정상성 평가
ticketing_count
ADF test statistic: -8.90366524755091
p-value: 1.1532103667357817e-14
p1
ADF test statistic: -7.316333641273258
p-value: 1.2265309593054393e-10
p2
ADF test statistic: -8.654863167499396
p-value: 5.0006128672818535e-14
학습 데이터 생성 및 예측데이터 담을 인덱스생성
ticketing_count p1 p2
date
2019/01/02 -2332.0 -0.591827 -1.436540
2019/01/03 1429.0 0.050071 0.043894
2019/01/04 590.0 0.032127 -0.160157
2019/01/05 11667.0 0.453149 0.965645
2019/01/06 -5564.0 0.215044 0.535168
... ... ... ...
2021/08/27 3970.0 0.335959 0.163582
2021/08/28 25874.0 -0.250823 1.778201
2021/08/29 -13585.0 0.145392 0.699918
2021/08/30 -28219.0 -0.665134 -2.349837
2021/08/31 4930.0 0.730510 -0.323811
[973 rows x 3 columns]
VAR예측모델 생성
C:\Users\USER\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:162: ValueWarning: No frequency information was provided, so inferred frequency D will be used.
% freq, ValueWarning)
AIC 확인
[15.90069559663783, 15.572796595323888, 15.438512269362898, 15.31754572453941, 14.474261894433562, 13.799665066727854, 13.669086564814712, 13.65876525492896, 13.656428475925214, 13.668539921532226, 13.681190675487326, 13.664512252200915, 13.506288204417192, 13.469633668260517, 13.479833314212462, 13.491712686201456, 13.507522473115344, 13.517533721136573, 13.514262134979266, 13.469163895291477, 13.443022659364134, 13.460504604688218, 13.473040467677867, 13.484688931315802, 13.495399070349762, 13.510175068018649, 13.49175709209404, 13.491702356470975, 13.501593114985997, 13.504594156560776, 13.505911140893375, 13.501083244172221, 13.49502634071845, 13.4825402365553, 13.485702287271796, 13.491493702045586, 13.501128156317233, 13.512071363254185, 13.51453437071208, 13.519701052802892, 13.520395791839347, 13.51838091174, 13.529890257546343, 13.54548322923905, 13.557810944948548, 13.577223037311466, 13.585091312860753, 13.584819969570573, 13.587481853395822]
최적값 확인
입력한153일 차분 예측
입력한153일 차분을 더해서 원래값 예측
예측값
ticketing_count p1 p2 ticketing_count_forecasted \
2021/09/01 8379.822447 -0.229706 -0.268557 16961.822447
2021/09/02 -3013.063096 0.169348 0.169535 13948.759351
2021/09/03 4792.668778 0.095916 0.373861 18741.428129
2021/09/04 20225.006527 0.386125 1.704444 38966.434656
2021/09/05 -10136.242784 -0.067023 0.371076 28830.191872
2021/09/06 -23969.370043 -0.595097 -1.715073 4860.821829
2021/09/07 2207.221154 0.116867 -0.389266 7068.042983
2021/09/08 9997.596857 -0.111956 -0.309473 17065.639841
2021/09/09 -3103.025116 0.169983 -0.047908 13962.614725
2021/09/10 3497.017794 0.133294 0.476790 17459.632518
2021/09/11 23318.455698 0.392189 1.695311 40778.088216
2021/09/12 -9992.588391 0.096094 0.364947 30785.499825
2021/09/13 -27126.774124 -0.756169 -1.930844 3658.725702
2021/09/14 5402.260793 0.162654 -0.177296 9060.986495
2021/09/15 7980.389948 -0.093019 -0.274704 17041.376443
2021/09/16 -2147.577998 0.162500 -0.038151 14893.798445
2021/09/17 3349.624443 0.125116 0.423708 18243.422888
2021/09/18 22830.461262 0.361325 1.516753 41073.884149
2021/09/19 -10955.898071 0.052654 0.258291 30117.986078
2021/09/20 -24617.505615 -0.769187 -1.710191 5500.480463
2021/09/21 3368.053573 0.093730 -0.261761 8868.534036
2021/09/22 8424.405369 -0.034282 -0.237434 17292.939405
2021/09/23 -2691.801564 0.160833 -0.045882 14601.137841
2021/09/24 4468.807709 0.125172 0.420745 19069.945550
2021/09/25 21571.036752 0.455940 1.509045 40640.982302
2021/09/26 -10743.534198 0.061135 0.244850 29897.448104
2021/09/27 -24241.424586 -0.733965 -1.600000 5656.023518
2021/09/28 3576.987366 0.022834 -0.269996 9233.010884
2021/09/29 7361.993877 -0.023275 -0.231131 16595.004761
2021/09/30 -2069.494762 0.124274 -0.086842 14525.509999
2021/10/01 4572.594305 0.130574 0.416923 19098.104303
2021/10/02 21339.093057 0.455097 1.450591 40437.197360
2021/10/03 -10720.175689 0.091146 0.258583 29717.021672
2021/10/04 -23775.400664 -0.729358 -1.526510 5941.621008
2021/10/05 3501.102912 0.012869 -0.276418 9442.723920
2021/10/06 7173.454032 -0.005277 -0.190599 16616.177951
2021/10/07 -1947.297792 0.122153 -0.077097 14668.880159
2021/10/08 5035.327328 0.131481 0.392593 19704.207487
2021/10/09 20553.235877 0.467202 1.392806 40257.443364
2021/10/10 -10834.257426 0.076547 0.240499 29423.185938
2021/10/11 -22969.411283 -0.714104 -1.458009 6453.774655
2021/10/12 3168.146878 -0.025118 -0.315309 9621.921533
2021/10/13 6893.794228 0.003841 -0.174083 16515.715762
2021/10/14 -1901.650353 0.101635 -0.089616 14614.065408
2021/10/15 5543.145464 0.134759 0.379056 20157.210873
2021/10/16 19855.822962 0.476856 1.358173 40013.033834
2021/10/17 -10689.890565 0.082074 0.252778 29323.143269
2021/10/18 -22491.905380 -0.687695 -1.390863 6831.237889
2021/10/19 3069.913079 -0.042919 -0.334210 9901.150968
2021/10/20 6463.356482 0.009572 -0.159292 16364.507451
2021/10/21 -1737.664753 0.085556 -0.102038 14626.842698
2021/10/22 5840.872891 0.132690 0.356385 20467.715588
2021/10/23 19257.049654 0.472577 1.314526 39724.765242
2021/10/24 -10628.591389 0.080870 0.257265 29096.173853
2021/10/25 -21914.112886 -0.667154 -1.332796 7182.060967
2021/10/26 2829.835764 -0.057478 -0.350505 10011.896732
2021/10/27 6296.239924 0.017496 -0.136921 16308.136655
2021/10/28 -1650.231408 0.077242 -0.102010 14657.905247
2021/10/29 6204.642371 0.135877 0.344514 20862.547618
2021/10/30 18621.592595 0.473049 1.280836 39484.140214
2021/10/31 -10489.812860 0.079202 0.261551 28994.327354
2021/11/01 -21421.572569 -0.644187 -1.281574 7572.754784
2021/11/02 2625.053683 -0.073407 -0.370973 10197.808467
2021/11/03 6059.448683 0.021407 -0.125962 16257.257151
2021/11/04 -1571.490431 0.064985 -0.110383 14685.766719
2021/11/05 6481.787052 0.135721 0.330192 21167.553772
2021/11/06 18061.173517 0.468901 1.248786 39228.727289
2021/11/07 -10322.311122 0.079095 0.268770 28906.416168
2021/11/08 -20942.333991 -0.621135 -1.230445 7964.082176
2021/11/09 2418.838618 -0.083494 -0.383234 10382.920795
2021/11/10 5862.048042 0.026044 -0.113691 16244.968837
2021/11/11 -1499.611080 0.056617 -0.116237 14745.357757
2021/11/12 6705.488707 0.135636 0.316222 21450.846464
2021/11/13 17520.063499 0.463062 1.215423 38970.909964
2021/11/14 -10153.340861 0.076888 0.270883 28817.569103
2021/11/15 -20493.446324 -0.600556 -1.186081 8324.122779
2021/11/16 2195.004980 -0.094204 -0.395681 10519.127759
2021/11/17 5702.602798 0.029444 -0.103217 16221.730558
2021/11/18 -1434.582380 0.049162 -0.120023 14787.148178
2021/11/19 6907.817891 0.136226 0.306758 21694.966069
2021/11/20 17017.254548 0.457738 1.186983 38712.220617
2021/11/21 -9951.741459 0.075976 0.274589 28760.479158
2021/11/22 -20066.967115 -0.579754 -1.143143 8693.512043
2021/11/23 1982.177266 -0.102993 -0.405984 10675.689309
2021/11/24 5543.628551 0.032150 -0.095890 16219.317860
2021/11/25 -1375.853904 0.042150 -0.125312 14843.463956
2021/11/26 7059.095358 0.135738 0.296523 21902.559315
2021/11/27 16549.245944 0.450951 1.158437 38451.805259
2021/11/28 -9742.189888 0.074590 0.276957 28709.615371
2021/11/29 -19652.604576 -0.560257 -1.102391 9057.010795
2021/11/30 1771.321672 -0.110460 -0.413296 10828.332467
2021/12/01 5405.398584 0.034720 -0.088540 16233.731050
2021/12/02 -1319.564516 0.036660 -0.128769 14914.166534
2021/12/03 7182.126635 0.135665 0.288165 22096.293170
2021/12/04 16105.823350 0.444250 1.131126 38202.116520
2021/12/05 -9525.019238 0.073267 0.277830 28677.097282
2021/12/06 -19259.626895 -0.541843 -1.064957 9417.470387
2021/12/07 1560.831889 -0.117451 -0.420205 10978.302276
2021/12/08 5272.281575 0.036442 -0.083307 16250.583851
2021/12/09 -1266.331349 0.031543 -0.132097 14984.252502
2021/12/10 7275.398952 0.135325 0.280900 22259.651454
2021/12/11 15690.542182 0.437359 1.105481 37950.193636
2021/12/12 -9298.814476 0.072221 0.278789 28651.379159
2021/12/13 -18876.977915 -0.524080 -1.028860 9774.401244
2021/12/14 1357.581466 -0.123307 -0.425235 11131.982710
2021/12/15 5145.740952 0.037919 -0.079014 16277.723661
2021/12/16 -1215.165907 0.027186 -0.135085 15062.557754
2021/12/17 7340.932311 0.134842 0.274011 22403.490065
2021/12/18 15296.960183 0.430250 1.080334 37700.450249
2021/12/19 -9069.853995 0.071079 0.278740 28630.596253
2021/12/20 -18506.184674 -0.507342 -0.994900 10124.411580
2021/12/21 1159.743914 -0.128568 -0.429103 11284.155493
2021/12/22 5025.386650 0.038964 -0.075432 16309.542143
2021/12/23 -1163.881602 0.023430 -0.137230 15145.660541
2021/12/24 7385.820299 0.134390 0.268173 22531.480840
2021/12/25 14924.552387 0.423239 1.056404 37456.033228
2021/12/26 -8837.865848 0.070137 0.278333 28618.167380
2021/12/27 -18146.023077 -0.491339 -0.962737 10472.144303
2021/12/28 967.611978 -0.133185 -0.432172 11439.756281
2021/12/29 4907.230640 0.039602 -0.072935 16346.986920
2021/12/30 -1113.827629 0.020078 -0.139171 15233.159292
2021/12/31 7410.184714 0.133775 0.262727 22643.344006
2022/01/01 14570.460883 0.416170 1.033303 37213.804889
2022/01/02 -8604.947025 0.069255 0.277625 28608.857864
2022/01/03 -17794.168636 -0.476078 -0.932016 10814.689228
2022/01/04 782.608271 -0.137179 -0.434191 11597.297499
2022/01/05 4792.184127 0.039968 -0.070997 16389.481626
2022/01/06 -1063.884192 0.017208 -0.140616 15325.597434
2022/01/07 7417.603206 0.133131 0.257739 22743.200640
2022/01/08 14232.500629 0.409160 1.010942 36975.701269
2022/01/09 -8372.768195 0.068453 0.276507 28602.933074
2022/01/10 -17450.881111 -0.461541 -0.902882 11152.051963
2022/01/11 604.094429 -0.140688 -0.435541 11756.146392
2022/01/12 4678.851432 0.040010 -0.069704 16434.997824
2022/01/13 -1013.962524 0.014686 -0.141659 15421.035300
2022/01/14 7410.628765 0.132421 0.253199 22831.664065
2022/01/15 13909.711306 0.402221 0.989430 36741.375370
2022/01/16 -8141.674337 0.067761 0.275243 28599.701034
2022/01/17 -17114.791067 -0.447625 -0.875053 11484.909967
2022/01/18 432.570926 -0.143701 -0.436219 11917.480893
2022/01/19 4566.802820 0.039805 -0.068967 16484.283714
2022/01/20 -964.276826 0.012490 -0.142412 15520.006888
2022/01/21 7390.774849 0.131643 0.248910 22910.781737
2022/01/22 13600.160789 0.395341 0.968583 36510.942526
2022/01/23 -7912.924526 0.067133 0.273782 28598.018000
2022/01/24 -16785.552945 -0.434327 -0.848475 11812.465055
2022/01/25 268.053241 -0.146290 -0.436292 12080.518296
2022/01/26 4455.959161 0.039371 -0.068652 16536.477457
2022/01/27 -914.553270 0.010588 -0.142824 15621.924186
2022/01/28 7360.165414 0.130823 0.244902 22982.089600
2022/01/29 13302.829305 0.388557 0.948416 36284.918905
2022/01/30 -7687.077840 0.066588 0.272187 28597.841065
2022/01/31 -16462.840285 -0.421590 -0.823079 12135.000780
p1_forecasted p2_forecasted
2021/09/01 4.847317 -3.658249
2021/09/02 5.016665 -3.488714
2021/09/03 5.112581 -3.114854
2021/09/04 5.498707 -1.410409
2021/09/05 5.431684 -1.039333
2021/09/06 4.836586 -2.754407
2021/09/07 4.953453 -3.143673
2021/09/08 4.841497 -3.453146
2021/09/09 5.011480 -3.501054
2021/09/10 5.144774 -3.024264
2021/09/11 5.536963 -1.328953
2021/09/12 5.633057 -0.964006
2021/09/13 4.876888 -2.894850
2021/09/14 5.039541 -3.072146
2021/09/15 4.946523 -3.346850
2021/09/16 5.109023 -3.385002
2021/09/17 5.234139 -2.961294
2021/09/18 5.595463 -1.444541
2021/09/19 5.648117 -1.186250
2021/09/20 4.878930 -2.896441
2021/09/21 4.972660 -3.158202
2021/09/22 4.938378 -3.395635
2021/09/23 5.099211 -3.441517
2021/09/24 5.224383 -3.020772
2021/09/25 5.680323 -1.511727
2021/09/26 5.741458 -1.266877
2021/09/27 5.007493 -2.866877
2021/09/28 5.030328 -3.136873
2021/09/29 5.007053 -3.368004
2021/09/30 5.131327 -3.454846
2021/10/01 5.261901 -3.037923
2021/10/02 5.716997 -1.587332
2021/10/03 5.808143 -1.328749
2021/10/04 5.078785 -2.855259
2021/10/05 5.091655 -3.131677
2021/10/06 5.086377 -3.322276
2021/10/07 5.208530 -3.399374
2021/10/08 5.340010 -3.006781
2021/10/09 5.807212 -1.613975
2021/10/10 5.883759 -1.373475
2021/10/11 5.169655 -2.831484
2021/10/12 5.144538 -3.146793
2021/10/13 5.148378 -3.320876
2021/10/14 5.250013 -3.410492
2021/10/15 5.384772 -3.031435
2021/10/16 5.861629 -1.673262
2021/10/17 5.943702 -1.420484
2021/10/18 5.256007 -2.811347
2021/10/19 5.213087 -3.145557
2021/10/20 5.222659 -3.304849
2021/10/21 5.308215 -3.406887
2021/10/22 5.440905 -3.050502
2021/10/23 5.913482 -1.735976
2021/10/24 5.994352 -1.478710
2021/10/25 5.327198 -2.811506
2021/10/26 5.269720 -3.162011
2021/10/27 5.287216 -3.298933
2021/10/28 5.364458 -3.400943
2021/10/29 5.500334 -3.056428
2021/10/30 5.973384 -1.775592
2021/10/31 6.052586 -1.514041
2021/11/01 5.408399 -2.795615
2021/11/02 5.334992 -3.166588
2021/11/03 5.356399 -3.292550
2021/11/04 5.421385 -3.402933
2021/11/05 5.557106 -3.072741
2021/11/06 6.026006 -1.823955
2021/11/07 6.105102 -1.555185
2021/11/08 5.483966 -2.785630
2021/11/09 5.400472 -3.168864
2021/11/10 5.426516 -3.282555
2021/11/11 5.483133 -3.398792
2021/11/12 5.618770 -3.082571
2021/11/13 6.081832 -1.867147
2021/11/14 6.158719 -1.596264
2021/11/15 5.558163 -2.782345
2021/11/16 5.463959 -3.178026
2021/11/17 5.493403 -3.281243
2021/11/18 5.542564 -3.401266
2021/11/19 5.678790 -3.094508
2021/11/20 6.136528 -1.907525
2021/11/21 6.212504 -1.632936
2021/11/22 5.632750 -2.776078
2021/11/23 5.529758 -3.182062
2021/11/24 5.561907 -3.277952
2021/11/25 5.604057 -3.403264
2021/11/26 5.739795 -3.106742
2021/11/27 6.190746 -1.948305
2021/11/28 6.265337 -1.671348
2021/11/29 5.705080 -2.773739
2021/11/30 5.594620 -3.187035
2021/12/01 5.629341 -3.275575
2021/12/02 5.666001 -3.404344
2021/12/03 5.801666 -3.116179
2021/12/04 6.245915 -1.985053
2021/12/05 6.319182 -1.707223
2021/12/06 5.777339 -2.772180
2021/12/07 5.659889 -3.192385
2021/12/08 5.696331 -3.275692
2021/12/09 5.727873 -3.407789
2021/12/10 5.863199 -3.126889
2021/12/11 6.300558 -2.021408
2021/12/12 6.372779 -1.742619
2021/12/13 5.848698 -2.771479
2021/12/14 5.725391 -3.196714
2021/12/15 5.763310 -3.275727
2021/12/16 5.790496 -3.410812
2021/12/17 5.925339 -3.136801
2021/12/18 6.355588 -2.056467
2021/12/19 6.426667 -1.777727
2021/12/20 5.919325 -2.772628
2021/12/21 5.790756 -3.201730
2021/12/22 5.829721 -3.277162
2021/12/23 5.853151 -3.414392
2021/12/24 5.987540 -3.146220
2021/12/25 6.410780 -2.089816
2021/12/26 6.480917 -1.811483
2021/12/27 5.989578 -2.774220
2021/12/28 5.856393 -3.206391
2021/12/29 5.895995 -3.279326
2021/12/30 5.916073 -3.418498
2021/12/31 6.049848 -3.155771
2022/01/01 6.466018 -2.122468
2022/01/02 6.535273 -1.844843
2022/01/03 6.059195 -2.776859
2022/01/04 5.922016 -3.211051
2022/01/05 5.961984 -3.282048
2022/01/06 5.979192 -3.422664
2022/01/07 6.112323 -3.164925
2022/01/08 6.521483 -2.153983
2022/01/09 6.589936 -1.877476
2022/01/10 6.128395 -2.780358
2022/01/11 5.987707 -3.215899
2022/01/12 6.027717 -3.285604
2022/01/13 6.042403 -3.427262
2022/01/14 6.174825 -3.174063
2022/01/15 6.577046 -2.184633
2022/01/16 6.644807 -1.909390
2022/01/17 6.197182 -2.784443
2022/01/18 6.053481 -3.220662
2022/01/19 6.093285 -3.289629
2022/01/20 6.105776 -3.432041
2022/01/21 6.237419 -3.183131
2022/01/22 6.632761 -2.214548
2022/01/23 6.699893 -1.940766
2022/01/24 6.265566 -2.789241
2022/01/25 6.119276 -3.225533
2022/01/26 6.158647 -3.294186
2022/01/27 6.169235 -3.437010
2022/01/28 6.300059 -3.192108
2022/01/29 6.688616 -2.243692
2022/01/30 6.755204 -1.971505
2022/01/31 6.333615 -2.794584 
Author And Source
이 문제에 관하여([공모전 수상작 리뷰] Reactjs+Nodejs+python+scikit-learn{ PCA(주성분 분석), VAR(다변량시계열분석)}으로 공연 예매 추이 시나리오 별 예측하는 서비스 만들어보기 - 데이터 분석 편(3)), 우리는 이곳에서 더 많은 자료를 발견하고 링크를 클릭하여 보았다 https://velog.io/@designc/공모전-수상작-리뷰-ReactjsNodejspythonscikit-learn-PCA주성분-분석-VAR다변량시계열분석으로-공연-예매-추이-시나리오-별-예측하는-서비스-만들어보기-데이터-분석-편3저자 귀속: 원작자 정보가 원작자 URL에 포함되어 있으며 저작권은 원작자 소유입니다.
우수한 개발자 콘텐츠 발견에 전념
(Collection and Share based on the CC Protocol.)
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