Python pmdarima将对象分配给自动\u arima输出

我正在使用auto_arima进行实验，它提供了最佳模型的良好输出，可用于时间序列预测

从pmdarima导入自动\u arima

stepwise_fit = auto_arima(hourly_avg['kW'], start_p=0, start_q=0,
                          max_p=2, max_q=2, m=4,
                          seasonal=False,
                          d=None, trace=True,
                          error_action='ignore',   # we don't want to know if an order does not work
                          suppress_warnings=True,  # we don't want convergence warnings
                          stepwise=True)           # set to stepwise

stepwise_fit.summary()

输出：

Performing stepwise search to minimize aic
 ARIMA(0,0,0)(0,0,0)[0]             : AIC=778.328, Time=0.01 sec
 ARIMA(1,0,0)(0,0,0)[0]             : AIC=inf, Time=0.07 sec
 ARIMA(0,0,1)(0,0,0)[0]             : AIC=inf, Time=0.07 sec
 ARIMA(1,0,1)(0,0,0)[0]             : AIC=138.016, Time=0.12 sec
 ARIMA(2,0,1)(0,0,0)[0]             : AIC=135.913, Time=0.16 sec
 ARIMA(2,0,0)(0,0,0)[0]             : AIC=inf, Time=0.11 sec
 ARIMA(2,0,2)(0,0,0)[0]             : AIC=135.302, Time=0.27 sec
 ARIMA(1,0,2)(0,0,0)[0]             : AIC=138.299, Time=0.14 sec
 ARIMA(2,0,2)(0,0,0)[0] intercept   : AIC=121.020, Time=0.36 sec
 ARIMA(1,0,2)(0,0,0)[0] intercept   : AIC=123.032, Time=0.36 sec
 ARIMA(2,0,1)(0,0,0)[0] intercept   : AIC=119.824, Time=0.28 sec
 ARIMA(1,0,1)(0,0,0)[0] intercept   : AIC=125.968, Time=0.31 sec
 ARIMA(2,0,0)(0,0,0)[0] intercept   : AIC=118.512, Time=0.15 sec
 ARIMA(1,0,0)(0,0,0)[0] intercept   : AIC=130.956, Time=0.12 sec

Best model:  ARIMA(2,0,0)(0,0,0)[0] intercept
Total fit time: 2.547 seconds

这里没有太多的智慧，对此我深表歉意，但有可能为最佳拟合模型分配一个变量吗？还是必须从上述输出中手动选择

ARIMA（2,0,0）

，才能继续使用时间序列预测方法

例如，一些变量，如

best\u model=best model:ARIMA（2,0,0）

最佳选择是什么…

：

model = pm.auto_arima(train, seasonal=False)

# make your forecasts
forecasts = model.predict(24)  # predict N steps into the future