问题描述
我正在尝试使用auto_arima，它可以很好地输出用于时间序列预测的最佳模型。
from pmdarima import auto_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] : AIC=778.328,Time=0.01 sec
ARIMA(1,0)[0] : AIC=inf,Time=0.07 sec
ARIMA(0,1)(0,Time=0.07 sec
ARIMA(1,0)[0] : AIC=138.016,Time=0.12 sec
ARIMA(2,0)[0] : AIC=135.913,Time=0.16 sec
ARIMA(2,Time=0.11 sec
ARIMA(2,2)(0,0)[0] : AIC=135.302,Time=0.27 sec
ARIMA(1,0)[0] : AIC=138.299,Time=0.14 sec
ARIMA(2,0)[0] intercept : AIC=121.020,Time=0.36 sec
ARIMA(1,0)[0] intercept : AIC=123.032,Time=0.36 sec
ARIMA(2,0)[0] intercept : AIC=119.824,Time=0.28 sec
ARIMA(1,0)[0] intercept : AIC=125.968,Time=0.31 sec
ARIMA(2,0)[0] intercept : AIC=118.512,Time=0.15 sec
ARIMA(1,0)[0] intercept : AIC=130.956,Time=0.12 sec
Best model: ARIMA(2,0)[0] intercept
Total fit time: 2.547 seconds
这里我没有道歉，但是可以为最佳拟合模型分配变量吗？还是必须从上面的输出中手动选择ARIMA(2,0)才能继续其时间序列预测方法？
例如，像best_model = Best model: ARIMA(2,0)这样的变量，最好的选择是...
解决方法
Look at the documentation where they give an example：
model = pm.auto_arima(train,seasonal=False)
# make your forecasts
forecasts = model.predict(24) # predict N steps into the future