I am trying to forecast the electric load, using python SARIMAX. I use the load history and temperature profile (as the exogenous input). Both load and temperature have the seasonality of 96 (the sample time is 15 min =seasonality is daily) as you can see for 6 days in the following figure
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so I used 96 step difference to make both time series stationary and used the obtained data data to train ARIMA model (4,0,4). Finally I tested the model for the next day of the operation the one step forecasting is very good however the dynamic forecasting seems to be awful. I know that the dynamic forecasting could not be as good as one step forecasting, however I believe that this big mismatch is not related to this point. I really Appreciate if somebody helps me to find my possible mistake. Also the (80 steps) out of sample forecast results for the differentiated signal is shown bellow
enter image description here

  • $\begingroup$ Your arima model seems strange ( probably bad due to software limitations) .perhaps that is why you are having problems $\endgroup$ – IrishStat Aug 20 '17 at 15:44

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