ETS Forecasting high Root Mean Squared Error
Posted: Sat Aug 05, 2017 3:21 pm
Hi Eviews team,
I performed a ETS month-ahead daily demand forecast using daily values from May 2016 in order to predict daily values of June 2016.
The output looks very good when I plot the series on top of the actual values. Also the MAPE I calculated manually shows 2.59%, which is very good. But I don't understand why the RMSE on the output table (below) is over 1:
Sample: 5/01/2016 5/31/2016
Included observations: 31
Model: A,N,A
Model selection: Akaike Information Criterion
Convergence achieved on boundaries.
Parameters
Alpha: 0.000000
Gamma: 0.000000
Compact Log-likelihood -264.9415
Log-likelihood -255.7018
Akaike Information Criterion 561.8831
Schwarz Criterion 584.8269
Hannan-Quinn Criterion 569.3622
Sum of Squared Residuals 26509188
Root Mean Squared Error 924.7352
Average Mean Squared Error 903048.3
Any ideas why?? Thanks so much in advance!
I performed a ETS month-ahead daily demand forecast using daily values from May 2016 in order to predict daily values of June 2016.
The output looks very good when I plot the series on top of the actual values. Also the MAPE I calculated manually shows 2.59%, which is very good. But I don't understand why the RMSE on the output table (below) is over 1:
Sample: 5/01/2016 5/31/2016
Included observations: 31
Model: A,N,A
Model selection: Akaike Information Criterion
Convergence achieved on boundaries.
Parameters
Alpha: 0.000000
Gamma: 0.000000
Compact Log-likelihood -264.9415
Log-likelihood -255.7018
Akaike Information Criterion 561.8831
Schwarz Criterion 584.8269
Hannan-Quinn Criterion 569.3622
Sum of Squared Residuals 26509188
Root Mean Squared Error 924.7352
Average Mean Squared Error 903048.3
Any ideas why?? Thanks so much in advance!