ETS Forecasting high Root Mean Squared Error

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Ricardo98
Posts: 14
Joined: Tue Jun 20, 2017 12:49 pm

ETS Forecasting high Root Mean Squared Error

Postby Ricardo98 » 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!

startz
Non-normality and collinearity are NOT problems!
Posts: 3175
Joined: Wed Sep 17, 2008 2:25 pm

Re: ETS Forecasting high Root Mean Squared Error

Postby startz » Sat Aug 05, 2017 3:42 pm

What is the typical size of your variable being forecast?

Ricardo98
Posts: 14
Joined: Tue Jun 20, 2017 12:49 pm

Re: ETS Forecasting high Root Mean Squared Error

Postby Ricardo98 » Sat Aug 05, 2017 3:46 pm

Hi Chris,

The mean of my series is 28231. Is not supposed RMSE to fall between 0 and 1? Or at least that's what I found among other examples of ETS.

Thanks,

startz
Non-normality and collinearity are NOT problems!
Posts: 3175
Joined: Wed Sep 17, 2008 2:25 pm

Re: ETS Forecasting high Root Mean Squared Error

Postby startz » Sat Aug 05, 2017 3:52 pm

Nope. RMSE has the same units as the original variable.

Ricardo98
Posts: 14
Joined: Tue Jun 20, 2017 12:49 pm

Re: ETS Forecasting high Root Mean Squared Error

Postby Ricardo98 » Sat Aug 05, 2017 4:01 pm

Ok, thanks Chris!

One more question, in order to visualize the quality of my forecasting I am calculating scalars involving the forecasted and actual series such as MAPE(%) as @mape(actual,forecasted).

Any other one appart from @rmse that you would recommend?

Thanks again for the help.


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