자기귀모형

• 자기회귀과정: 예측값이 이전 값에만 선형적으로 의존한다고 가정

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

plt.rcParams['font.family'] = 'Noto Sans KR'

df = pd.read_csv('_data/foot.csv')

from statsmodels.tsa.stattools import adfuller

ADF_result = adfuller(df['foot_traffic'])
ADF_result[0], ADF_result[1]

(np.float64(-1.1758885999240747), np.float64(0.683880891789619))

• 비 정상성 발견. 차분 진행

foot_diff = np.diff(df['foot_traffic'], n=1)

ADF_result = adfuller(foot_diff)
ADF_result[0], ADF_result[1]

(np.float64(-5.268231347422044), np.float64(6.369317654781239e-06))

from statsmodels.graphics.tsaplots import plot_acf

plot_acf(foot_diff, lags=20)
plt.show()

from statsmodels.tsa.arima_process import ArmaProcess

ma2 = np.array([1, 0, 0])
ar2 = np.array([1, -0.33, -0.50])
AR2_process = ArmaProcess(ar2, ma2).generate_sample(nsample=1000)

from statsmodels.graphics.tsaplots import plot_pacf

plot_pacf(AR2_process, lags=20)
plt.show()

plot_pacf(foot_diff, lags=20)
plt.show()

df_diff = pd.DataFrame({'foot_traffic_diff': foot_diff})

train = df_diff.iloc[:-52]
test = df_diff.iloc[-52:]