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
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)(0,0,0)[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?还是必须从上面的输出中手动选择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_model = Best model: ARIMA(2,0,0)
最好的选择是什么
Look at the documentation where they give an example:
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