매출

[iammiori]

3. 카드매출

• 전처리

  
  import pandas as pd
  import numpy as np
  import matplotlib.pyplot as plt
  import seaborn as sns

card_df = pd.read_csv(r'C:\Users\ejr93\Desktop\빅콘\가공데이터\card_df.txt', sep = '\t') card_df.head()

card_df['STD_DD'] = card_df['STD_DD'].astype(str) card_df['STD_DD'] = card_df['STD_DD'].apply(lambda x:x[0:4]+'-'+x[4:6]+'-'+x[6:8] ) card_df['STD_DD'] = card_df['STD_DD'].astype('datetime64[ns]') card_df['GU_CD'] = card_df['GU_CD'].astype(str) card_df['DONG_CD'] = card_df['DONG_CD'].astype(str) card_df['GU_DONG'] = card_df['GU_CD'] + card_df['DONG_CD'] card_df['GU_DONG'] = card_df['GU_DONG'].astype(int)

def function1(x): if x == 'M': b = '0' elif x == 'F': b = '1' return b

card_df['SEX_CD'] = card_df['SEX_CD'].apply(lambda x: function1(x))

def function2(x): if x == 110515: a = '청운효자동' elif x == 110530: a = '사직동' elif x == 110540: a = '삼청동' elif x == 110550: a = '부암동' elif x == 110560: a = '평창동' elif x == 110570: a = '무악동' elif x == 110580: a = '교남동' elif x == 110600: a = '가회동' elif x == 110615: a = '종로1.2.3.4가동' elif x == 110630: a = '종로5.6가동' elif x == 110640: a = '이화동' elif x == 110650: a = '혜화동' elif x == 110670: a = '창신1동' elif x == 110680: a = '창신2동' elif x == 110690: a = '창신3동' elif x == 110700: a = '숭인1동' elif x == 110710: a = '숭인2동' elif x == 350560: a = '월계1동' elif x == 350570: a = '월계2동' elif x == 350580: a = '월계3동' elif x == 350595: a = '공릉1동' elif x == 350600: a = '공릉2동' elif x == 350611: a = '하계1동' elif x == 350612: a = '하계2동' elif x == 350619: a = '중계본동' elif x == 350621: a = '중계1동' elif x == 350624: a = '중계4동' elif x == 350625: a = '중계2.3동' elif x == 350630: a = '상계1동' elif x == 350640: a = '상계2동' elif x == 350665: a = '상계3.4동' elif x == 350670: a = '상계5동' elif x == 350695: a = '상계6.7동' elif x == 350700: a = '상계8동' elif x == 350710: a = '상계9동' elif x == 350720: a = '상계10동' return a

card_df['DONG_CD'] = card_df['GU_DONG'].apply(lambda x: function2(x))

def function3(x): if x == '110': c = '종로구' elif x == '350': c = '노원구' return c card_df['GU_CD'] = card_df['GU_CD'].apply(lambda x: function3(x)) card_df.drop('GU_DONG', axis = 1, inplace = True) card_df.head() card_df.to_csv('C:\Users\ejr93\Desktop\빅콘\가공데이터\card_ppc_df.csv', sep = ',')

[iammiori]

• 미세먼지 - 매출액 (전체행정동)

  
  import pandas as pd
  import numpy as np
  import seaborn as sns
  import matplotlib.pyplot as plt
  %matplotlib inline
  import statsmodels.api as sm
  finedust_day1_df = pd.read_csv(r'C:\\Users\\ejr93\\Desktop\\빅콘\\가공데이터\\finedust_day1.csv', encoding = 'CP949')
  finedust_day1_df.head(365)

finedust_day1_df.rename(columns = {'tm' : 'time'}, inplace = True)

def function1(x): if x == '종로1,2,3,4가동': a = '종로1.2.3.4가동' elif x == '종로5,6가동': a = '종로5.6가동' elif x == '중계2,3동': a = '중계2.3동' elif x == '상계3,4동': a = '상계3.4동' elif x == '상계6,7동': a = '상계6.7동' else: a = x return a

finedust_day1_df['dong'] = finedust_day1_df['dong'].apply(lambda x: function1(x)) finedust_day1_df.head() card_df.drop('Unnamed: 0', axis = 1, inplace = True) card_df.rename(columns = {'STD_DD' : 'time', 'GU_CD' : 'gu', 'DONG_CD' : 'dong'}, inplace = True) card_df['time'] = card_df['time'].astype(str) card_dust_df = pd.merge(card_df, finedust_day1_df, on = ['time', 'dong']) card_dust_df.info()

card_dust_df.drop(['gu_x','dong', 'SEX_CD', 'AGE_CD','Unnamed: 0', 'gu_y', 'humi', 'noise', 'pm25', 'temp', 'yoil', 'weekend','pm25_class', 'pm10_class'], axis = 1, inplace = True) card_dust_group_df = card_dust_df.groupby(by = ['time', 'MCT_CAT_CD']).agg({'USE_CNT' : 'sum', 'USE_AMT' : 'sum', 'pm10' : 'mean'}) card_dust_group_df.reset_index(inplace=True) card_dust_group_df.head()

card_dust_group_10_df = card_dust_group_df[card_dust_group_df['MCT_CAT_CD'] == 10] card_dust_group_20_df = card_dust_group_df[card_dust_group_df['MCT_CAT_CD'] == 20] card_dust_group_21_df = card_dust_group_df[card_dust_group_df['MCT_CAT_CD'] == 21] card_dust_group_22_df = card_dust_group_df[card_dust_group_df['MCT_CAT_CD'] == 22] card_dust_group_30_df = card_dust_group_df[card_dust_group_df['MCT_CAT_CD'] == 30] card_dust_group_31_df = card_dust_group_df[card_dust_group_df['MCT_CAT_CD'] == 31] card_dust_group_32_df = card_dust_group_df[card_dust_group_df['MCT_CAT_CD'] == 32] card_dust_group_33_df = card_dust_group_df[card_dust_group_df['MCT_CAT_CD'] == 33] card_dust_group_34_df = card_dust_group_df[card_dust_group_df['MCT_CAT_CD'] == 34] card_dust_group_35_df = card_dust_group_df[card_dust_group_df['MCT_CAT_CD'] == 35] card_dust_group_40_df = card_dust_group_df[card_dust_group_df['MCT_CAT_CD'] == 40] card_dust_group_42_df = card_dust_group_df[card_dust_group_df['MCT_CAT_CD'] == 42] card_dust_group_43_df = card_dust_group_df[card_dust_group_df['MCT_CAT_CD'] == 43] card_dust_group_44_df = card_dust_group_df[card_dust_group_df['MCT_CAT_CD'] == 44] card_dust_group_50_df = card_dust_group_df[card_dust_group_df['MCT_CAT_CD'] == 50] card_dust_group_52_df = card_dust_group_df[card_dust_group_df['MCT_CAT_CD'] == 52] card_dust_group_60_df = card_dust_group_df[card_dust_group_df['MCT_CAT_CD'] == 60] card_dust_group_62_df = card_dust_group_df[card_dust_group_df['MCT_CAT_CD'] == 62] card_dust_group_70_df = card_dust_group_df[card_dust_group_df['MCT_CAT_CD'] == 70] card_dust_group_71_df = card_dust_group_df[card_dust_group_df['MCT_CAT_CD'] == 71] card_dust_group_80_df = card_dust_group_df[card_dust_group_df['MCT_CAT_CD'] == 80] card_dust_group_81_df = card_dust_group_df[card_dust_group_df['MCT_CAT_CD'] == 81] card_dust_group_92_df = card_dust_group_df[card_dust_group_df['MCT_CAT_CD'] == 92]

card_dust_group_10_df.reset_index(inplace=True) card_dust_group_20_df.reset_index(inplace=True) card_dust_group_21_df.reset_index(inplace=True) card_dust_group_22_df.reset_index(inplace=True) card_dust_group_30_df.reset_index(inplace=True) card_dust_group_31_df.reset_index(inplace=True) card_dust_group_32_df.reset_index(inplace=True) card_dust_group_33_df.reset_index(inplace=True) card_dust_group_34_df.reset_index(inplace=True) card_dust_group_35_df.reset_index(inplace=True) card_dust_group_40_df.reset_index(inplace=True) card_dust_group_42_df.reset_index(inplace=True) card_dust_group_43_df.reset_index(inplace=True) card_dust_group_44_df.reset_index(inplace=True) card_dust_group_50_df.reset_index(inplace=True) card_dust_group_52_df.reset_index(inplace=True) card_dust_group_60_df.reset_index(inplace=True) card_dust_group_62_df.reset_index(inplace=True) card_dust_group_70_df.reset_index(inplace=True) card_dust_group_71_df.reset_index(inplace=True) card_dust_group_80_df.reset_index(inplace=True) card_dust_group_81_df.reset_index(inplace=True) card_dust_group_92_df.reset_index(inplace=True)

plt.figure(figsize=(8,6), dpi=80) plt.plot(card_dust_group_21_df['pm10'], color = 'blue')

from statsmodels.tsa.stattools import adfuller

10

모든 변수 분산 안정화 변환

pm10_10_log = np.log1p(card_dust_group_10_df['pm10']) USE_AMT_10_log = np.log1p(card_dust_group_10_df['USE_AMT']) adfuller(pm10_10_log) # 10 log_pm10 : 비정상 시계열 pm10_10_log_diff1 =np.diff(pm10_10_log) adfuller(pm10_10_log_diff1) #log_pm10 1차 차분 : 정상시계열 plt.figure(figsize=(10,5), dpi=80) plt.plot(pm10_10_log_diff1, color = 'blue') #log_pm10 1차 차분 plot plt.figure(figsize=(10,5), dpi=80) plt.plot(card_dust_group_10_df['USE_AMT'], color = 'blue') adfuller(USE_AMT_10_log) # 10 매출액 : 정상시계열 USE_AMT_10_log_diff1 = np.diff(USE_AMT_10_log) adfuller(USE_AMT_10_log_diff1) # 10 매출액 1차 차분: 정상시계열

from statsmodels.graphics.tsaplots import plot_acf, plot_pacf fig,ax = plt.subplots(2,1,figsize=(15,8)) fig = sm.graphics.tsa.plot_acf(USE_AMT_10_log_diff1, lags=50, ax=ax[0]) fig = sm.graphics.tsa.plot_pacf(USE_AMT_10_log_diff1, lags=50, ax=ax[1]) plt.show() model_10 = sm.tsa.SARIMAX(USE_AMT_10_log, order=(1,1,1), seasonal_order=(3,1,0,7), exog =pm10_10_log) results_10 = model_10.fit() print (results_10.summary()) res_10 = results_10.resid fig,ax = plt.subplots(2,1,figsize=(15,8)) fig = sm.graphics.tsa.plot_acf(res_10, lags=50, ax=ax[0]) fig = sm.graphics.tsa.plot_pacf(res_10, lags=50, ax=ax[1]) plt.show()

20

모든 변수 분산 안정화 변환

pm10_20_log = np.log1p(card_dust_group_20_df['pm10']) USE_AMT_20_log = np.log1p(card_dust_group_20_df['USE_AMT']) adfuller(pm10_20_log) # 20 log_pm10 : 비정상 시계열 pm10_20_log_diff1 =np.diff(pm10_20_log) adfuller(pm10_20_log_diff1) #log_pm10 1차 차분 : 정상시계열 plt.figure(figsize=(10,5), dpi=80) plt.plot(pm10_20_log_diff1, color = 'blue') #log_pm10 1차 차분 plot plt.figure(figsize=(10,5), dpi=80) plt.plot(card_dust_group_20_df['USE_AMT'], color = 'blue') adfuller(USE_AMT_20_log) # 20 매출액 : 정상시계열(유의수준 5%) USE_AMT_20_log_diff1 = np.diff(USE_AMT_20_log) adfuller(USE_AMT_20_log_diff1) #20 매출액 1차 차분 : 정상시계열 fig,ax = plt.subplots(2,1,figsize=(15,8)) fig = sm.graphics.tsa.plot_acf(USE_AMT_20_log_diff1, lags=50, ax=ax[0]) fig = sm.graphics.tsa.plot_pacf(USE_AMT_20_log_diff1, lags=50, ax=ax[1]) plt.show() model_20 = sm.tsa.SARIMAX(USE_AMT_20_log, order=(1,1,1), seasonal_order=(0,1,1,7), exog = pm10_20_log) results_20 = model_20.fit() print (results_20.summary()) res_20 = results_20.resid fig,ax = plt.subplots(2,1,figsize=(15,8)) fig = sm.graphics.tsa.plot_acf(res_20, lags=50, ax=ax[0]) fig = sm.graphics.tsa.plot_pacf(res_20, lags=50, ax=ax[1]) plt.show()

22

모든 변수 분산 안정화 변환

pm10_22_log = np.log1p(card_dust_group_22_df['pm10']) USE_AMT_22_log = np.log1p(card_dust_group_22_df['USE_AMT']) adfuller(pm10_22_log) # 22 log_pm10 : 비정상 시계열 pm10_22_log_diff1 =np.diff(pm10_22_log) adfuller(pm10_22_log_diff1) #log_pm10 1차 차분 : 정상시계열 plt.figure(figsize=(10,5), dpi=80) plt.plot(pm10_22_log_diff1, color = 'blue') #log_pm10 1차 차분 plot plt.figure(figsize=(10,5), dpi=80) plt.plot(card_dust_group_22_df['USE_AMT'], color = 'blue') adfuller(USE_AMT_22_log) # 22 매출액 : 정상시계열(유의수준 5%) USE_AMT_22_log_diff1 = np.diff(USE_AMT_22_log) adfuller(USE_AMT_22_log_diff1) #42 매출액 1차 차분 : 정상시계열 fig,ax = plt.subplots(2,1,figsize=(15,8)) fig = sm.graphics.tsa.plot_acf(USE_AMT_22_log_diff1, lags=50, ax=ax[0]) fig = sm.graphics.tsa.plot_pacf(USE_AMT_22_log_diff1, lags=50, ax=ax[1]) plt.show() model_22 = sm.tsa.SARIMAX(USE_AMT_22_log, order=(0,1,2), seasonal_order=(0,1,1,7), exog = pm10_22_log) results_22 = model_22.fit() print (results_22.summary()) res_22 = results_22.resid fig,ax = plt.subplots(2,1,figsize=(15,8)) fig = sm.graphics.tsa.plot_acf(res_22, lags=50, ax=ax[0]) fig = sm.graphics.tsa.plot_pacf(res_22, lags=50, ax=ax[1]) plt.show()

62

모든 변수 분산 안정화 변환

pm10_62_log = np.log1p(card_dust_group_62_df['pm10']) USE_AMT_62_log = np.log1p(card_dust_group_62_df['USE_AMT']) adfuller(pm10_62_log) # 62 log_pm10 : 비정상 시계열 pm10_62_log_diff1 =np.diff(pm10_62_log) adfuller(pm10_62_log_diff1) #log_pm10 1차 차분 : 정상시계열 plt.figure(figsize=(10,5), dpi=80) plt.plot(pm10_62_log_diff1, color = 'blue') #log_pm10 1차 차분 plot plt.figure(figsize=(10,5), dpi=80) plt.plot(card_dust_group_62_df['USE_AMT'], color = 'blue') adfuller(USE_AMT_62_log) # 62 매출액 : 정상시계열 USE_AMT_62_log_diff1 = np.diff(USE_AMT_62_log) adfuller(USE_AMT_62_log_diff1) #62 매출액 1차 차분 : 정상시계열 fig,ax = plt.subplots(2,1,figsize=(15,8)) fig = sm.graphics.tsa.plot_acf(USE_AMT_62_log_diff1, lags=50, ax=ax[0]) fig = sm.graphics.tsa.plot_pacf(USE_AMT_62_log_diff1, lags=50, ax=ax[1]) plt.show() model_62 = sm.tsa.SARIMAX(USE_AMT_62_log, order=(1,1,1), seasonal_order=(0,1,1,7), exog =pm10_62_log) results_62 = model_62.fit() print (results_62.summary()) res_62 = results_62.resid fig,ax = plt.subplots(2,1,figsize=(15,8)) fig = sm.graphics.tsa.plot_acf(res_62, lags=50, ax=ax[0]) fig = sm.graphics.tsa.plot_pacf(res_62, lags=50, ax=ax[1]) plt.show()

70

모든 변수 분산 안정화 변환

pm10_70_log = np.log1p(card_dust_group_70_df['pm10']) USE_AMT_70_log = np.log1p(card_dust_group_70_df['USE_AMT']) adfuller(pm10_70_log) # 70 log_pm10 : 비정상 시계열 pm10_70_log_diff1 =np.diff(pm10_70_log) adfuller(pm10_70_log_diff1) #log_pm10 1차 차분 : 정상시계열 plt.figure(figsize=(10,5), dpi=80) plt.plot(pm10_70_log_diff1, color = 'blue') #log_pm10 1차 차분 plot plt.figure(figsize=(10,5), dpi=80) plt.plot(card_dust_group_70_df['USE_AMT'], color = 'blue') adfuller(USE_AMT_70_log) # 70 매출액 : 정상시계열 USE_AMT_70_log_diff1 = np.diff(USE_AMT_70_log) adfuller(USE_AMT_70_log_diff1) #70 매출액 1차 차분 : 정상시계열 fig,ax = plt.subplots(2,1,figsize=(15,8)) fig = sm.graphics.tsa.plot_acf(USE_AMT_70_log_diff1, lags=50, ax=ax[0]) fig = sm.graphics.tsa.plot_pacf(USE_AMT_70_log_diff1, lags=50, ax=ax[1]) plt.show() model_70 = sm.tsa.SARIMAX(USE_AMT_70_log, order=(1,1,1), seasonal_order=(0,1,1,7), exog =pm10_70_log) results_70 = model_70.fit() print (results_70.summary()) res_70 = results_70.resid fig,ax = plt.subplots(2,1,figsize=(15,8)) fig = sm.graphics.tsa.plot_acf(res_70, lags=50, ax=ax[0]) fig = sm.graphics.tsa.plot_pacf(res_70, lags=50, ax=ax[1]) plt.show()

[iammiori]

• 미세먼지언급량 - 매출액 (전체행정동)

  
  import pandas as pd
  import numpy as np
  import seaborn as sns
  import matplotlib.pyplot as plt
  %matplotlib inline
  import statsmodels.api as sm

card_df = pd.read_csv(r'C:\Users\ejr93\Desktop\빅콘\가공데이터\card_ppc_df.csv') card_df.head()

card_df.drop('Unnamed: 0', axis = 1, inplace = True) card_df.rename(columns = {'STD_DD' : 'time', 'GU_CD' : 'gu', 'DONG_CD' : 'dong'}, inplace = True) card_df['time'] = card_df['time'].astype(str) dust_count_df = pd.read_csv(r'C:\Users\ejr93\Desktop\빅콘\가공데이터\dust_count.csv', encoding = 'CP949') dust_count_df.head() card_sns_df = pd.merge(card_df, dust_count_df, on = ['time']) card_sns_df.head()

card_sns_df.drop(['gu','dong','USE_CNT' ,'SEX_CD', 'AGE_CD', 'fine_total', 'new_total'], axis = 1, inplace = True) card_sns_df.head()

card_sns_group_df = card_sns_df.groupby(by = ['time', 'MCT_CAT_CD']).agg({'USE_AMT' : 'sum', 'cnt_total' : 'mean'}) card_sns_group_df.reset_index(inplace=True) card_sns_group_df.head() card_sns_group_df.describe()

card_sns_group_10_df = card_sns_group_df[card_sns_group_df['MCT_CAT_CD'] == 10] card_sns_group_20_df = card_sns_group_df[card_sns_group_df['MCT_CAT_CD'] == 20] card_sns_group_21_df = card_sns_group_df[card_sns_group_df['MCT_CAT_CD'] == 21] card_sns_group_22_df = card_sns_group_df[card_sns_group_df['MCT_CAT_CD'] == 22] card_sns_group_30_df = card_sns_group_df[card_sns_group_df['MCT_CAT_CD'] == 30] card_sns_group_31_df = card_sns_group_df[card_sns_group_df['MCT_CAT_CD'] == 31] card_sns_group_32_df = card_sns_group_df[card_sns_group_df['MCT_CAT_CD'] == 32] card_sns_group_33_df = card_sns_group_df[card_sns_group_df['MCT_CAT_CD'] == 33] card_sns_group_34_df = card_sns_group_df[card_sns_group_df['MCT_CAT_CD'] == 34] card_sns_group_35_df = card_sns_group_df[card_sns_group_df['MCT_CAT_CD'] == 35] card_sns_group_40_df = card_sns_group_df[card_sns_group_df['MCT_CAT_CD'] == 40] card_sns_group_42_df = card_sns_group_df[card_sns_group_df['MCT_CAT_CD'] == 42] card_sns_group_43_df = card_sns_group_df[card_sns_group_df['MCT_CAT_CD'] == 43] card_sns_group_44_df = card_sns_group_df[card_sns_group_df['MCT_CAT_CD'] == 44] card_sns_group_50_df = card_sns_group_df[card_sns_group_df['MCT_CAT_CD'] == 50] card_sns_group_52_df = card_sns_group_df[card_sns_group_df['MCT_CAT_CD'] == 52] card_sns_group_60_df = card_sns_group_df[card_sns_group_df['MCT_CAT_CD'] == 60] card_sns_group_62_df = card_sns_group_df[card_sns_group_df['MCT_CAT_CD'] == 62] card_sns_group_70_df = card_sns_group_df[card_sns_group_df['MCT_CAT_CD'] == 70] card_sns_group_71_df = card_sns_group_df[card_sns_group_df['MCT_CAT_CD'] == 71] card_sns_group_80_df = card_sns_group_df[card_sns_group_df['MCT_CAT_CD'] == 80] card_sns_group_81_df = card_sns_group_df[card_sns_group_df['MCT_CAT_CD'] == 81] card_sns_group_92_df = card_sns_group_df[card_sns_group_df['MCT_CAT_CD'] == 92]

card_sns_group_10_df.reset_index(inplace=True) card_sns_group_20_df.reset_index(inplace=True) card_sns_group_21_df.reset_index(inplace=True) card_sns_group_22_df.reset_index(inplace=True) card_sns_group_30_df.reset_index(inplace=True) card_sns_group_31_df.reset_index(inplace=True) card_sns_group_32_df.reset_index(inplace=True) card_sns_group_33_df.reset_index(inplace=True) card_sns_group_34_df.reset_index(inplace=True) card_sns_group_35_df.reset_index(inplace=True) card_sns_group_40_df.reset_index(inplace=True) card_sns_group_42_df.reset_index(inplace=True) card_sns_group_43_df.reset_index(inplace=True) card_sns_group_44_df.reset_index(inplace=True) card_sns_group_50_df.reset_index(inplace=True) card_sns_group_52_df.reset_index(inplace=True) card_sns_group_60_df.reset_index(inplace=True) card_sns_group_62_df.reset_index(inplace=True) card_sns_group_70_df.reset_index(inplace=True) card_sns_group_71_df.reset_index(inplace=True) card_sns_group_80_df.reset_index(inplace=True) card_sns_group_81_df.reset_index(inplace=True) card_sns_group_92_df.reset_index(inplace=True) card_sns_group_10_df.head()

plt.figure(figsize=(8,5), dpi=80) plt.plot(card_sns_group_21_df['cnt_total'], color = 'blue')

from statsmodels.tsa.stattools import adfuller

10분석

모든 변수 분산 안정화 변환

cnt_total_10_log = np.log1p(card_sns_group_10_df['cnt_total']) USE_AMT_10_log = np.log1p(card_sns_group_10_df['USE_AMT']) adfuller(cnt_total_10_log) # 10 log_미세먼지 언급량 : 비정상 시계열 cnt_total_10_log_diff1 =np.diff(cnt_total_10_log) adfuller(cnt_total_10_log_diff1) #log_미세먼지 언급량 1차 차분 : 정상시계열 plt.figure(figsize=(10,5), dpi=80) plt.plot(cnt_total_10_log_diff1, color = 'blue') #log_미세먼지 언급량 1차 차분 plot plt.figure(figsize=(10,5), dpi=80) plt.plot(card_sns_group_10_df['USE_AMT'], color = 'blue') adfuller(USE_AMT_10_log) # 10 매출액 : 정상시계열 USE_AMT_10_log_diff1 = np.diff(USE_AMT_10_log) adfuller(USE_AMT_10_log_diff1) # 10 매출액 1차 차분: 정상시계열

from statsmodels.graphics.tsaplots import plot_acf, plot_pacf fig,ax = plt.subplots(2,1,figsize=(15,8)) fig = sm.graphics.tsa.plot_acf(USE_AMT_10_log_diff1, lags=50, ax=ax[0]) fig = sm.graphics.tsa.plot_pacf(USE_AMT_10_log_diff1, lags=50, ax=ax[1]) plt.show()

model_10 = sm.tsa.SARIMAX(USE_AMT_10_log, order=(1,1,1), seasonal_order=(3,1,0,7), exog =cnt_total_10_log) results_10 = model_10.fit() print (results_10.summary()) res_10 = results_10.resid fig,ax = plt.subplots(2,1,figsize=(15,8)) fig = sm.graphics.tsa.plot_acf(res_10, lags=50, ax=ax[0]) fig = sm.graphics.tsa.plot_pacf(res_10, lags=50, ax=ax[1]) plt.show()

20 분석

모든 변수 분산 안정화 변환

cnt_total_20_log = np.log1p(card_sns_group_20_df['cnt_total']) USE_AMT_20_log = np.log1p(card_sns_group_20_df['USE_AMT']) adfuller(cnt_total_20_log) # 20 log_미세먼지 언급량 : 비정상 시계열 import statsmodels cnt_total_20_log_diff1 =np.diff(cnt_total_20_log) adfuller(cnt_total_20_log_diff1) #log_미세먼지 언급량 1차 차분 : 정상시계열 plt.figure(figsize=(10,5), dpi=80) plt.plot(cnt_total_20_log_diff1, color = 'blue') #log_미세먼지 언급량 1차 차분 plot plt.figure(figsize=(10,5), dpi=80) plt.plot(card_sns_group_20_df['USE_AMT'], color = 'blue') adfuller(USE_AMT_20_log) # 20 매출액 : 정상시계열(유의수준 5%) USE_AMT_20_log_diff1 = np.diff(USE_AMT_20_log) adfuller(USE_AMT_20_log_diff1) #20 매출액 1차 차분 : 정상시계열 fig,ax = plt.subplots(2,1,figsize=(15,8)) fig = sm.graphics.tsa.plot_acf(USE_AMT_20_log_diff1, lags=50, ax=ax[0]) fig = sm.graphics.tsa.plot_pacf(USE_AMT_20_log_diff1, lags=50, ax=ax[1]) plt.show() model_20 = sm.tsa.SARIMAX(USE_AMT_20_log, order=(1,1,1), seasonal_order=(0,1,1,7), exog =cnt_total_20_log) results_20 = model_20.fit() print (results_20.summary()) res_20 = results_20.resid fig,ax = plt.subplots(2,1,figsize=(15,8)) fig = sm.graphics.tsa.plot_acf(res_20, lags=50, ax=ax[0]) fig = sm.graphics.tsa.plot_pacf(res_20, lags=50, ax=ax[1]) plt.show()

21분석

모든 변수 분산 안정화 변환

cnt_total_21_log = np.log1p(card_sns_group_21_df['cnt_total']) USE_AMT_21_log = np.log1p(card_sns_group_21_df['USE_AMT']) adfuller(cnt_total_21_log) # 21 log_미세먼지 언급량 : 비정상 시계열 cnt_total_21_log_diff1 =np.diff(cnt_total_21_log) adfuller(cnt_total_21_log_diff1) #log_미세먼지 언급량 1차 차분 : 정상시계열 plt.figure(figsize=(10,5), dpi=80) plt.plot(cnt_total_21_log_diff1, color = 'blue') #log_미세먼지 언급량 1차 차분 plot plt.figure(figsize=(10,5), dpi=80) plt.plot(card_sns_group_21_df['USE_AMT'], color = 'blue') adfuller(USE_AMT_21_log) # 21 매출액 : 정상시계열 USE_AMT_21_log_diff1 = np.diff(USE_AMT_21_log) adfuller(USE_AMT_21_log_diff1) #42 매출액 1차 차분 : 정상시계열 plt.figure(figsize=(10,5), dpi=80) plt.plot(USE_AMT_21_log_diff1, color = 'blue') fig,ax = plt.subplots(2,1,figsize=(15,8)) fig = sm.graphics.tsa.plot_acf(USE_AMT_21_log_diff1, lags=50, ax=ax[0]) fig = sm.graphics.tsa.plot_pacf(USE_AMT_21_log_diff1, lags=50, ax=ax[1]) plt.show() model_21 = sm.tsa.SARIMAX(USE_AMT_21_log, order=(1,1,1), seasonal_order=(1,1,2,7), exog =cnt_total_10_log) results_21 = model_21.fit() print (results_21.summary()) res_21 = results_21.resid fig,ax = plt.subplots(2,1,figsize=(15,8)) fig = sm.graphics.tsa.plot_acf(res_21, lags=50, ax=ax[0]) fig = sm.graphics.tsa.plot_pacf(res_21, lags=50, ax=ax[1]) plt.show()

22분석

모든 변수 분산 안정화 변환

cnt_total_22_log = np.log1p(card_sns_group_22_df['cnt_total']) USE_AMT_22_log = np.log1p(card_sns_group_22_df['USE_AMT']) adfuller(cnt_total_22_log) # 22 log_미세먼지 언급량 : 비정상 시계열 cnt_total_22_log_diff1 =np.diff(cnt_total_22_log) adfuller(cnt_total_22_log_diff1) #log_미세먼지 언급량 1차 차분 : 정상시계열 plt.figure(figsize=(10,5), dpi=80) plt.plot(cnt_total_22_log_diff1, color = 'blue') #log_미세먼지 언급량 1차 차분 plot plt.figure(figsize=(10,5), dpi=80) plt.plot(card_sns_group_22_df['USE_AMT'], color = 'blue') adfuller(USE_AMT_22_log) # 22 매출액 : 정상시계열(유의수준 5%) USE_AMT_22_log_diff1 = np.diff(USE_AMT_22_log) adfuller(USE_AMT_22_log_diff1) #42 매출액 1차 차분 : 정상시계열 fig,ax = plt.subplots(2,1,figsize=(15,8)) fig = sm.graphics.tsa.plot_acf(USE_AMT_22_log_diff1, lags=50, ax=ax[0]) fig = sm.graphics.tsa.plot_pacf(USE_AMT_22_log_diff1, lags=50, ax=ax[1]) plt.show() model_22 = sm.tsa.SARIMAX(USE_AMT_22_log, order=(1,1,2), seasonal_order=(1,1,0,7), exog =cnt_total_22_log) results_22 = model_22.fit() print (results_22.summary()) res_22 = results_22.resid fig,ax = plt.subplots(2,1,figsize=(15,8)) fig = sm.graphics.tsa.plot_acf(res_22, lags=50, ax=ax[0]) fig = sm.graphics.tsa.plot_pacf(res_22, lags=50, ax=ax[1]) plt.show()

33분석

모든 변수 분산 안정화 변환

cnt_total_33_log = np.log1p(card_sns_group_33_df['cnt_total']) USE_AMT_33_log = np.log1p(card_sns_group_33_df['USE_AMT']) adfuller(cnt_total_33_log) # 33 log_미세먼지 언급량 : 비정상 시계열 cnt_total_33_log_diff1 =np.diff(cnt_total_33_log) adfuller(cnt_total_33_log_diff1) #log_미세먼지 언급량 1차 차분 : 정상시계열 plt.figure(figsize=(10,5), dpi=80) plt.plot(cnt_total_33_log_diff1, color = 'blue') #log_미세먼지 언급량 1차 차분 plot plt.figure(figsize=(10,5), dpi=80) plt.plot(USE_AMT_33_log, color = 'blue') adfuller(USE_AMT_33_log) # 33 매출액 : 비정상시계열 statsmodels.tsa.stattools.coint(USE_AMT_33_log, cnt_total_33_log, trend = 'nc') #H0 : "공적분 존재 안함" 기각 못함 USE_AMT_33_log_diff1 = np.diff(USE_AMT_33_log) adfuller(USE_AMT_33_log_diff1) #33 매출액 1차 차분 : 정상시계열 plt.figure(figsize=(10,5), dpi=80) plt.plot(USE_AMT_33_log_diff1, color = 'blue') fig,ax = plt.subplots(2,1,figsize=(15,8)) fig = sm.graphics.tsa.plot_acf(USE_AMT_33_log_diff1, lags=50, ax=ax[0]) fig = sm.graphics.tsa.plot_pacf(USE_AMT_33_log_diff1, lags=50, ax=ax[1]) plt.show() model_33 = sm.tsa.SARIMAX(USE_AMT_33_log, order=(1,1,1), seasonal_order=(1,1,1,7), exog =cnt_total_33_log) results_33 = model_33.fit() print (results_33.summary()) res_33 = results_33.resid fig,ax = plt.subplots(2,1,figsize=(15,8)) fig = sm.graphics.tsa.plot_acf(res_33, lags=50, ax=ax[0]) fig = sm.graphics.tsa.plot_pacf(res_33, lags=50, ax=ax[1]) plt.show()

40분석

모든 변수 분산 안정화 변환

cnt_total_40_log = np.log1p(card_sns_group_40_df['cnt_total']) USE_AMT_40_log = np.log1p(card_sns_group_40_df['USE_AMT']) adfuller(cnt_total_40_log) # 40 log_미세먼지 언급량 : 비정상 시계열 cnt_total_40_log_diff1 =np.diff(cnt_total_40_log) adfuller(cnt_total_40_log_diff1) #log_미세먼지 언급량 1차 차분 : 정상시계열 plt.figure(figsize=(10,5), dpi=80) plt.plot(cnt_total_40_log_diff1, color = 'blue') #log_미세먼지 언급량 1차 차분 plot plt.figure(figsize=(10,5), dpi=80) plt.plot(card_sns_group_40_df['USE_AMT'], color = 'blue') adfuller(USE_AMT_40_log) # 40 매출액 : 정상시계열(유의수준 5%) USE_AMT_40_log_diff1 = np.diff(USE_AMT_40_log) adfuller(USE_AMT_40_log_diff1) #40 매출액 1차 차분 : 정상시계열 fig,ax = plt.subplots(2,1,figsize=(15,8)) fig = sm.graphics.tsa.plot_acf(USE_AMT_40_log_diff1, lags=50, ax=ax[0]) fig = sm.graphics.tsa.plot_pacf(USE_AMT_40_log_diff1, lags=50, ax=ax[1]) plt.show() model_40 = sm.tsa.SARIMAX(USE_AMT_40_log, order=(1,1,1), seasonal_order=(1,1,1,7), exog =cnt_total_40_log) results_40 = model_40.fit() print (results_40.summary()) res_40 = results_40.resid fig,ax = plt.subplots(2,1,figsize=(15,8)) fig = sm.graphics.tsa.plot_acf(res_40, lags=50, ax=ax[0]) fig = sm.graphics.tsa.plot_pacf(res_40, lags=50, ax=ax[1]) plt.show()

42

모든 변수 분산 안정화 변환

cnt_total_42_log = np.log1p(card_sns_group_42_df['cnt_total']) USE_AMT_42_log = np.log1p(card_sns_group_42_df['USE_AMT']) adfuller(cnt_total_42_log) # 42 log_미세먼지 언급량 : 비정상 시계열 cnt_total_42_log_diff1 =np.diff(cnt_total_42_log) adfuller(cnt_total_42_log_diff1) #log_미세먼지 언급량 1차 차분 : 정상시계열 plt.figure(figsize=(10,5), dpi=80) plt.plot(cnt_total_42_log_diff1, color = 'blue') #log_미세먼지 언급량 1차 차분 plot plt.figure(figsize=(10,5), dpi=80) plt.plot(card_sns_group_42_df['USE_AMT'], color = 'blue') adfuller(USE_AMT_42_log) # 42 매출액 : 비정상 시계열 statsmodels.tsa.stattools.coint(USE_AMT_42_log, cnt_total_42_log, trend = 'nc') #H0 : "공적분 존재 안함" 기각 못함 USE_AMT_42_log_diff1 = np.diff(USE_AMT_42_log) adfuller(USE_AMT_42_log_diff1) #42 매출액 1차 차분 : 정상시계열 fig,ax = plt.subplots(2,1,figsize=(15,8)) fig = sm.graphics.tsa.plot_acf(USE_AMT_42_log_diff1, lags=50, ax=ax[0]) fig = sm.graphics.tsa.plot_pacf(USE_AMT_42_log_diff1, lags=50, ax=ax[1]) plt.show() model_42 = sm.tsa.SARIMAX(USE_AMT_42_log, order=(1,1,1), seasonal_order=(0,1,1,7), exog =cnt_total_42_log) results_42 = model_42.fit() print (results_42.summary()) res_42 = results_42.resid fig,ax = plt.subplots(2,1,figsize=(15,8)) fig = sm.graphics.tsa.plot_acf(res_42, lags=50, ax=ax[0]) fig = sm.graphics.tsa.plot_pacf(res_42, lags=50, ax=ax[1]) plt.show()

43분석

모든 변수 분산 안정화 변환

cnt_total_43_log = np.log1p(card_sns_group_43_df['cnt_total']) USE_AMT_43_log = np.log1p(card_sns_group_43_df['USE_AMT']) adfuller(cnt_total_43_log) # 43 log_미세먼지 언급량 : 비정상 시계열 cnt_total_43_log_diff1 =np.diff(cnt_total_43_log) adfuller(cnt_total_43_log_diff1) #log_미세먼지 언급량 1차 차분 : 정상시계열 plt.figure(figsize=(10,5), dpi=80) plt.plot(cnt_total_43_log_diff1, color = 'blue') #log_미세먼지 언급량 1차 차분 plot plt.figure(figsize=(10,5), dpi=80) plt.plot(card_sns_group_43_df['USE_AMT'], color = 'blue') adfuller(USE_AMT_43_log) # 43 매출액 : 정상시계열 USE_AMT_43_log_diff1 = np.diff(USE_AMT_43_log) adfuller(USE_AMT_43_log_diff1) #43 매출액 1차 차분 : 정상시계열 fig,ax = plt.subplots(2,1,figsize=(15,8)) fig = sm.graphics.tsa.plot_acf(USE_AMT_43_log_diff1, lags=50, ax=ax[0]) fig = sm.graphics.tsa.plot_pacf(USE_AMT_43_log_diff1, lags=50, ax=ax[1]) plt.show() model_43 = sm.tsa.SARIMAX(USE_AMT_43_log, order=(1,1,1), seasonal_order=(1,1,1,7), exog =cnt_total_43_log) results_43 = model_43.fit() print (results_43.summary()) res_43 = results_43.resid fig,ax = plt.subplots(2,1,figsize=(15,8)) fig = sm.graphics.tsa.plot_acf(res_43, lags=50, ax=ax[0]) fig = sm.graphics.tsa.plot_pacf(res_43, lags=50, ax=ax[1]) plt.show()

44분석

모든 변수 분산 안정화 변환

cnt_total_44_log = np.log1p(card_sns_group_44_df['cnt_total']) USE_AMT_44_log = np.log1p(card_sns_group_44_df['USE_AMT']) adfuller(cnt_total_44_log) # 44 log_미세먼지 언급량 : 비정상 시계열 cnt_total_44_log_diff1 =np.diff(cnt_total_44_log) adfuller(cnt_total_44_log_diff1) #log_미세먼지 언급량 1차 차분 : 정상시계열 plt.figure(figsize=(10,5), dpi=80) plt.plot(cnt_total_44_log_diff1, color = 'blue') #log_미세먼지 언급량 1차 차분 plot plt.figure(figsize=(10,5), dpi=80) plt.plot(card_sns_group_44_df['USE_AMT'], color = 'blue') adfuller(USE_AMT_44_log) # 44 매출액 : 정상시계열 USE_AMT_44_log_diff1 = np.diff(USE_AMT_44_log) adfuller(USE_AMT_44_log_diff1) #43 매출액 1차 차분 : 정상시계열 fig,ax = plt.subplots(2,1,figsize=(15,8)) fig = sm.graphics.tsa.plot_acf(USE_AMT_44_log_diff1, lags=50, ax=ax[0]) fig = sm.graphics.tsa.plot_pacf(USE_AMT_44_log_diff1, lags=50, ax=ax[1]) plt.show() model_44 = sm.tsa.SARIMAX(USE_AMT_44_log, order=(1,1,1), seasonal_order=(1,1,1,7), exog =cnt_total_44_log) results_44 = model_44.fit() print (results_44.summary()) res_44 = results_44.resid fig,ax = plt.subplots(2,1,figsize=(15,8)) fig = sm.graphics.tsa.plot_acf(res_44, lags=50, ax=ax[0]) fig = sm.graphics.tsa.plot_pacf(res_44, lags=50, ax=ax[1]) plt.show()

50분석

모든 변수 분산 안정화 변환

cnt_total_50_log = np.log1p(card_sns_group_50_df['cnt_total']) USE_AMT_50_log = np.log1p(card_sns_group_50_df['USE_AMT']) adfuller(cnt_total_50_log) # 50 log_미세먼지 언급량 : 비정상 시계열 cnt_total_50_log_diff1 =np.diff(cnt_total_50_log) adfuller(cnt_total_50_log_diff1) #log_미세먼지 언급량 1차 차분 : 정상시계열 plt.figure(figsize=(10,5), dpi=80) plt.plot(cnt_total_50_log_diff1, color = 'blue') #log_미세먼지 언급량 1차 차분 plot plt.figure(figsize=(10,5), dpi=80) plt.plot(card_sns_group_50_df['USE_AMT'], color = 'blue') adfuller(USE_AMT_50_log) # 50 매출액 : 비정상시계열 statsmodels.tsa.stattools.coint(USE_AMT_50_log, cnt_total_50_log, trend = 'nc') #H0 : "공적분 존재 안함" 기각 못함 USE_AMT_50_log_diff1 = np.diff(USE_AMT_50_log) adfuller(USE_AMT_50_log_diff1) #50 매출액 1차 차분 : 정상시계열 fig,ax = plt.subplots(2,1,figsize=(15,8)) fig = sm.graphics.tsa.plot_acf(USE_AMT_50_log_diff1, lags=50, ax=ax[0]) fig = sm.graphics.tsa.plot_pacf(USE_AMT_50_log_diff1, lags=50, ax=ax[1]) plt.show() model_50 = sm.tsa.SARIMAX(USE_AMT_50_log, order=(1,1,1), seasonal_order=(1,1,1,7), exog =cnt_total_50_log) results_50 = model_50.fit() print (results_50.summary()) res_50 = results_50.resid fig,ax = plt.subplots(2,1,figsize=(15,8)) fig = sm.graphics.tsa.plot_acf(res_50, lags=50, ax=ax[0]) fig = sm.graphics.tsa.plot_pacf(res_50, lags=50, ax=ax[1]) plt.show()

62분석

모든 변수 분산 안정화 변환

cnt_total_62_log = np.log1p(card_sns_group_62_df['cnt_total']) USE_AMT_62_log = np.log1p(card_sns_group_62_df['USE_AMT']) adfuller(cnt_total_62_log) # 62 log_미세먼지 언급량 : 비정상 시계열 cnt_total_62_log_diff1 =np.diff(cnt_total_62_log) adfuller(cnt_total_62_log_diff1) #log_미세먼지 언급량 1차 차분 : 정상시계열 plt.figure(figsize=(10,5), dpi=80) plt.plot(cnt_total_62_log_diff1, color = 'blue') #log_미세먼지 언급량 1차 차분 plot plt.figure(figsize=(10,5), dpi=80) plt.plot(card_sns_group_62_df['USE_AMT'], color = 'blue') adfuller(USE_AMT_62_log) # 62 매출액 : 정상시계열 USE_AMT_62_log_diff1 = np.diff(USE_AMT_62_log) adfuller(USE_AMT_62_log_diff1) #62 매출액 1차 차분 : 정상시계열 fig,ax = plt.subplots(2,1,figsize=(15,8)) fig = sm.graphics.tsa.plot_acf(USE_AMT_62_log_diff1, lags=50, ax=ax[0]) fig = sm.graphics.tsa.plot_pacf(USE_AMT_62_log_diff1, lags=50, ax=ax[1]) plt.show() card_sns_group_62_df.head() model_62 = sm.tsa.SARIMAX(USE_AMT_62_log, order=(1,1,1), seasonal_order=(0,1,1,7), exog =cnt_total_62_log) results_62 = model_62.fit() print (results_62.summary()) res_62 = results_62.resid fig,ax = plt.subplots(2,1,figsize=(15,8)) fig = sm.graphics.tsa.plot_acf(res_62, lags=50, ax=ax[0]) fig = sm.graphics.tsa.plot_pacf(res_62, lags=50, ax=ax[1]) plt.show()

70분석

모든 변수 분산 안정화 변환

cnt_total_70_log = np.log1p(card_sns_group_70_df['cnt_total']) USE_AMT_70_log = np.log1p(card_sns_group_70_df['USE_AMT']) adfuller(cnt_total_70_log) # 70 log_미세먼지 언급량 : 비정상 시계열 cnt_total_70_log_diff1 =np.diff(cnt_total_70_log) adfuller(cnt_total_70_log_diff1) #log_미세먼지 언급량 1차 차분 : 정상시계열 plt.figure(figsize=(10,5), dpi=80) plt.plot(cnt_total_70_log_diff1, color = 'blue') #log_미세먼지 언급량 1차 차분 plot plt.figure(figsize=(10,5), dpi=80) plt.plot(card_sns_group_70_df['USE_AMT'], color = 'blue') adfuller(USE_AMT_70_log) # 70 매출액 : 정상시계열 USE_AMT_70_log_diff1 = np.diff(USE_AMT_70_log) adfuller(USE_AMT_70_log_diff1) #70 매출액 1차 차분 : 정상시계열 fig,ax = plt.subplots(2,1,figsize=(15,8)) fig = sm.graphics.tsa.plot_acf(USE_AMT_70_log_diff1, lags=50, ax=ax[0]) fig = sm.graphics.tsa.plot_pacf(USE_AMT_70_log_diff1, lags=50, ax=ax[1]) plt.show() model_70 = sm.tsa.SARIMAX(USE_AMT_70_log, order=(1,1,1), seasonal_order=(0,1,1,7), exog =cnt_total_70_log) results_70 = model_70.fit() print (results_70.summary()) res_70 = results_70.resid fig,ax = plt.subplots(2,1,figsize=(15,8)) fig = sm.graphics.tsa.plot_acf(res_70, lags=50, ax=ax[0]) fig = sm.graphics.tsa.plot_pacf(res_70, lags=50, ax=ax[1]) plt.show()

71분석

모든 변수 분산 안정화 변환

cnt_total_71_log = np.log1p(card_sns_group_71_df['cnt_total']) USE_AMT_71_log = np.log1p(card_sns_group_71_df['USE_AMT']) adfuller(cnt_total_71_log) # 71 log_미세먼지 언급량 : 비정상 시계열 cnt_total_71_log_diff1 =np.diff(cnt_total_71_log) adfuller(cnt_total_71_log_diff1) #log_미세먼지 언급량 1차 차분 : 정상시계열 plt.figure(figsize=(10,5), dpi=80) plt.plot(cnt_total_71_log_diff1, color = 'blue') #log_미세먼지 언급량 1차 차분 plot plt.figure(figsize=(10,5), dpi=80) plt.plot(card_sns_group_71_df['USE_AMT'], color = 'blue') adfuller(USE_AMT_71_log) # 71 매출액 : 정상시계열 USE_AMT_71_log_diff1 = np.diff(USE_AMT_71_log) adfuller(USE_AMT_71_log_diff1) #71 매출액 1차 차분 : 정상시계열 fig,ax = plt.subplots(2,1,figsize=(15,8)) fig = sm.graphics.tsa.plot_acf(USE_AMT_71_log_diff1, lags=50, ax=ax[0]) fig = sm.graphics.tsa.plot_pacf(USE_AMT_71_log_diff1, lags=50, ax=ax[1]) plt.show() model_71 = sm.tsa.SARIMAX(USE_AMT_71_log, order=(1,1,1), seasonal_order=(0,1,1,7), exog =cnt_total_71_log) results_71 = model_71.fit() print (results_71.summary()) res_71 = results_71.resid fig,ax = plt.subplots(2,1,figsize=(15,8)) fig = sm.graphics.tsa.plot_acf(res_71, lags=50, ax=ax[0]) fig = sm.graphics.tsa.plot_pacf(res_71, lags=50, ax=ax[1]) plt.show()

80분석

모든 변수 분산 안정화 변환

cnt_total_80_log = np.log1p(card_sns_group_80_df['cnt_total']) USE_AMT_80_log = np.log1p(card_sns_group_80_df['USE_AMT']) adfuller(cnt_total_80_log) # 80 log_미세먼지 언급량 : 비정상 시계열 cnt_total_80_log_diff1 =np.diff(cnt_total_80_log) adfuller(cnt_total_80_log_diff1) #log_미세먼지 언급량 1차 차분 : 정상시계열 plt.figure(figsize=(10,5), dpi=80) plt.plot(cnt_total_80_log_diff1, color = 'blue') #log_미세먼지 언급량 1차 차분 plot plt.figure(figsize=(10,5), dpi=80) plt.plot(card_sns_group_80_df['USE_AMT'], color = 'blue') adfuller(USE_AMT_80_log) # 80 매출액 : 정상시계열 USE_AMT_80_log_diff1 = np.diff(USE_AMT_80_log) adfuller(USE_AMT_80_log_diff1) #80 매출액 1차 차분 : 정상시계열 fig,ax = plt.subplots(2,1,figsize=(15,8)) fig = sm.graphics.tsa.plot_acf(USE_AMT_80_log_diff1, lags=50, ax=ax[0]) fig = sm.graphics.tsa.plot_pacf(USE_AMT_80_log_diff1, lags=50, ax=ax[1]) plt.show() model_80 = sm.tsa.SARIMAX(USE_AMT_80_log, order=(1,1,1), seasonal_order=(1,1,1,7), exog =cnt_total_80_log) results_80 = model_80.fit() print (results_80.summary()) res_80 = results_80.resid fig,ax = plt.subplots(2,1,figsize=(15,8)) fig = sm.graphics.tsa.plot_acf(res_80, lags=50, ax=ax[0]) fig = sm.graphics.tsa.plot_pacf(res_80, lags=50, ax=ax[1]) plt.show()

81분석

모든 변수 분산 안정화 변환

cnt_total_81_log = np.log1p(card_sns_group_81_df['cnt_total']) USE_AMT_81_log = np.log1p(card_sns_group_81_df['USE_AMT']) adfuller(cnt_total_81_log) # 81 log_미세먼지 언급량 : 비정상 시계열 cnt_total_81_log_diff1 =np.diff(cnt_total_81_log) adfuller(cnt_total_81_log_diff1) #log_미세먼지 언급량 1차 차분 : 정상시계열 plt.figure(figsize=(10,5), dpi=80) plt.plot(cnt_total_81_log_diff1, color = 'blue') #log_미세먼지 언급량 1차 차분 plot plt.figure(figsize=(10,5), dpi=80) plt.plot(card_sns_group_81_df['USE_AMT'], color = 'blue') adfuller(USE_AMT_81_log) # 81 매출액 : 정상시계열 USE_AMT_81_log_diff1 = np.diff(USE_AMT_81_log) adfuller(USE_AMT_81_log_diff1) #81 매출액 1차 차분 : 정상시계열 fig,ax = plt.subplots(2,1,figsize=(15,8)) fig = sm.graphics.tsa.plot_acf(USE_AMT_81_log_diff1, lags=50, ax=ax[0]) fig = sm.graphics.tsa.plot_pacf(USE_AMT_81_log_diff1, lags=50, ax=ax[1]) plt.show() model_81 = sm.tsa.SARIMAX(USE_AMT_81_log, order=(0,1,1), seasonal_order=(0,1,1,7), exog =cnt_total_81_log) results_81 = model_81.fit() print (results_81.summary()) res_81 = results_81.resid fig,ax = plt.subplots(2,1,figsize=(15,8)) fig = sm.graphics.tsa.plot_acf(res_81, lags=50, ax=ax[0]) fig = sm.graphics.tsa.plot_pacf(res_81, lags=50, ax=ax[1]) plt.show()

92분석

모든 변수 분산 안정화 변환

cnt_total_92_log = np.log1p(card_sns_group_92_df['cnt_total']) USE_AMT_92_log = np.log1p(card_sns_group_92_df['USE_AMT']) adfuller(cnt_total_92_log) # 92 log_미세먼지 언급량 : 비정상 시계열 cnt_total_92_log_diff1 =np.diff(cnt_total_92_log) adfuller(cnt_total_92_log_diff1) #log_미세먼지 언급량 1차 차분 : 정상시계열 plt.figure(figsize=(10,5), dpi=80) plt.plot(cnt_total_92_log_diff1, color = 'blue') #log_미세먼지 언급량 1차 차분 plot plt.figure(figsize=(10,5), dpi=80) plt.plot(card_sns_group_92_df['USE_AMT'], color = 'blue') adfuller(USE_AMT_92_log) # 92 매출액 : 비정상시계열 USE_AMT_92_log_diff1 = np.diff(USE_AMT_92_log) adfuller(USE_AMT_92_log_diff1) #92 매출액 1차 차분 : 정상시계열 fig,ax = plt.subplots(2,1,figsize=(15,8)) fig = sm.graphics.tsa.plot_acf(USE_AMT_92_log_diff1, lags=50, ax=ax[0]) fig = sm.graphics.tsa.plot_pacf(USE_AMT_92_log_diff1, lags=50, ax=ax[1]) plt.show() model_92 = sm.tsa.SARIMAX(USE_AMT_92_log, order=(1,1,1), seasonal_order=(0,1,1,7), exog =cnt_total_92_log) results_92 = model_92.fit() print (results_92.summary()) res_92 = results_92.resid fig,ax = plt.subplots(2,1,figsize=(15,8)) fig = sm.graphics.tsa.plot_acf(res_92, lags=50, ax=ax[0]) fig = sm.graphics.tsa.plot_pacf(res_92, lags=50, ax=ax[1]) plt.show()

[iammiori]

• 민감업종별 주요 행정동

  import pandas as pd
  import numpy as np
  import seaborn as sns
  import matplotlib.pyplot as plt
  %matplotlib inline
  import statsmodels.api as sm
  card_df = pd.read_csv(r'C:\\Users\\ejr93\\Desktop\\빅콘\\가공데이터\\card_ppc_df.csv')
  card_df.head()
  card_df.drop('Unnamed: 0', axis = 1, inplace = True)
  card_df.rename(columns = {'STD_DD' : 'time', 'GU_CD' : 'gu', 'DONG_CD' : 'dong'}, inplace = True)
  card_df['time'] = card_df['time'].astype(str)
  card_df.head()
  card_92_df = card_df[card_df['MCT_CAT_CD'] == 92]
  card_70_df = card_df[card_df['MCT_CAT_CD'] == 70]
  card_62_df = card_df[card_df['MCT_CAT_CD'] == 62]
  card_10_df = card_df[card_df['MCT_CAT_CD'] == 10]
  card_20_df = card_df[card_df['MCT_CAT_CD'] == 20]
  card_22_df = card_df[card_df['MCT_CAT_CD'] == 22]
  card_71_df = card_df[card_df['MCT_CAT_CD'] == 71]
  card_81_df = card_df[card_df['MCT_CAT_CD'] == 81]
  card_92_group_df = card_92_df.groupby(by = 'dong').sum()
  card_92_group_df = card_92_group_df.sort_values(['USE_AMT'], ascending=[False])
  card_92_group_df.head()
  card_70_group_df = card_70_df.groupby(by = 'dong').sum()
  card_70_group_df = card_70_group_df.sort_values(['USE_AMT'], ascending=[False])
  card_70_group_df.head()
  card_62_group_df = card_62_df.groupby(by = 'dong').sum()
  card_62_group_df = card_62_group_df.sort_values(['USE_AMT'], ascending=[False])
  card_62_group_df.head()
  card_10_group_df = card_10_df.groupby(by = 'dong').sum()
  card_10_group_df = card_10_group_df.sort_values(['USE_AMT'], ascending=[False])
  card_10_group_df.head()
  card_20_group_df = card_20_df.groupby(by = 'dong').sum()
  card_20_group_df = card_20_group_df.sort_values(['USE_AMT'], ascending=[False])
  card_20_group_df.head()
  card_22_group_df = card_22_df.groupby(by = 'dong').sum()
  card_22_group_df = card_22_group_df.sort_values(['USE_AMT'], ascending=[False])
  card_22_group_df.head()
  card_71_group_df = card_71_df.groupby(by = 'dong').sum()
  card_71_group_df = card_71_group_df.sort_values(['USE_AMT'], ascending=[False])
  card_71_group_df.head()
  card_81_group_df = card_81_df.groupby(by = 'dong').sum()
  card_81_group_df = card_81_group_df.sort_values(['USE_AMT'], ascending=[False])
  card_81_group_df.head()