Excel solver issues

Jack6627  wrote: ``I tried it and the results are a mixed bag compared my manually built Triple Exponential smoothing.``

I've read that FORECAST.ETS does additive seasonality, whereas your calculations do multiplitive seasonality.  But the source is not authoritative (i.e. MSFT documentation).  See the comments at the end of https://www.real-statistics.com/time-series-analysis/basic-time-series-forecasting/holt-winters-method .

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Jack6627  wrote:  ``The problem I have with using the function is that I don't have visibility into how the values of the smoothing coefficients are calculated, or the bucketing criteria for the seasonality``

I agree, to an extent.  I have a problem with the entire methodology.

As for the smoothing parameters, the FORECAST.ETS.STAT function returns their values.   But I, too, am concerned that we don't know how they are derived.  OTOH, the STAT function returns several measures of fit, including (S)MAPE.  They might give you some insight into the goodness of fit.

I'm not sure what you mean by "bucketing criteria" for seasonality.  If you mean the seasonal period (pattern length), the STAT function returns that, as well.  But note that it is only necessary if we enter 1 for seasonality.  We have the option to enter >1, which is our own knowledge of the seasonal period.

That said, I have a concern about that even when we enter >1.  I need to experiement with the algorithm and the Excel function to know if my concerns are valid.

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Jack6627  wrote:  ``my MAPE values are much closer to optimal with the Evolutionary solver than if I had used the GRG non-linear solver.``

I concur -- but only a little closer (5%).  But the real question is:  do the different smoothing parameters improve the forecast?  (Rhetorical.) 

With "your" example, I was surprised by the results with the smoothing parameters that GRG Nonlinear derives; but at least they are in the ballpark.  Not true with the parameters that "Evolutionary" derives, despite the smaller MAPE.  And as you said initially, who wants to wait 30+ sec for results -- for each of 300+ models?

However, I modified your example because I disagree with some of your formulas.  So  you and I are not looking at the same results.  YMMV.  Bottom line:  "to each his own".