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- sar_m = sarimax.SARIMAX(df_train,
- trend='n',
- order=(2,1,1),
- seasonal_order=(2, 1, 1, 24),
- enforce_stationarity=False,
- enforce_invertibility=False,
- simple_differencing=False).fit()
- # predict for every hour of the next month
- predict_steps = 24*30
- forecast = sar_m.forecast(steps=predict_steps)
- # plot against real data
- plot_forecast(df_test[:predict_steps], forecast,
- title='SARIMAX - Predicted vs Actual (September 2014)',
- xlabel='Day in September 2014',
- ylabel='Number of Pizza Orders')
- # calculate RMSE error
- rmse(df_test[:predict_steps].numOfOrders, forecast)
- date,numOfPizzaOrders
- 2014-04-01 00:00:00,12
- 2014-04-01 01:00:00,5
- 2014-04-01 02:00:00,2
- 2014-04-01 03:00:00,4
- 2014-04-01 04:00:00,3
- 2014-04-01 05:00:00,3
- 2014-04-01 06:00:00,7
- 2014-04-01 07:00:00,5
- 2014-04-01 08:00:00,17
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