Pythonで学ぶARIMAモデル
James Fulton
Climate informatics researcher
# Fit model model = ARIMA(df, order=(p,d,q)) results = model.fit() # Assign residuals to variable residuals = results.resid
# Fit model model = ARIMA(df, order=(p,d,q)) results = model.fit()
# Assign residuals to variable residuals = results.resid
2013-01-23 1.013129 2013-01-24 0.114055 2013-01-25 0.430698 2013-01-26 -1.247046 2013-01-27 -0.499565 ... ...
予測は実測からどれだけ離れているか？
mae = np.mean(np.abs(residuals))
モデルが適合していれば，残差は白色ガウス雑音になります。
# Create the 4 diagostics plots results.plot_diagnostics() plt.show()
print(results.summary())
... =================================================================================== Ljung-Box (Q): 32.10 Jarque-Bera (JB): 0.02 Prob(Q): 0.81 Prob(JB): 0.99 Heteroskedasticity (H): 1.28 Skew: -0.02 Prob(H) (two-sided): 0.21 Kurtosis: 2.98 ===================================================================================
Prob(Q)
Prob(JB)
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