# Selecting between exponential smoothing models: MAPE or AIC?

I have applied Exponential Smoothing methods on data (Quarterly electricity production in Australia million kilowatt hourly) and then I forecast the accuracy of my models,

ep_m<-msts(scan("e:\\DATA\\Quarterly electricity production in Australia million kilowatt hour.csv"),seasonal.periods = 4 )
ets(ep_m,"MMM")

ETS(M,M,M)

Call:
ets(y = ep_m, model = "MMM")

Smoothing parameters:
alpha = 0.5751
beta  = 0.0424
gamma = 0.3636

Initial states:
l = 4168.6162
b = 1.0194
s=0.9641 1.0806 1.0286 0.9267

sigma:  0.0164

AIC         AICc           BIC
2531.925    2532.911    2556.272

ets(ep_m,"MAM")

ETS(M,A,M(

Call:
ets(y = ep_m, model = "MAM")

Smoothing parameters:
alpha = 0.5574
beta  = 0.0634
gamma = 0.3732

Initial states:
l = 4172.3008
b = 90.599
s=0.9644 1.0815 1.025 0.9291

sigma:  0.0165

AIC                AICc                  BIC
2533.216     2534.202       2557.563
accuracy(ets(ep_m,"MMM"))

ME           RMSE            MAE               MPE                 MAPE           MASE            ACF1
Training set -69.33972   396.3885     274.0834     -0.2291014     1.316194     0.1901402    0.008993804
accuracy(ets(ep_m,"MAM"))

ME             RMSE            MAE               MPE                 MAPE           MASE             ACF1
Training set   1.494434   385.2168     269.2909      0.1453125      1.306918      0.1868155   -0.008601048


My Question: If I compare the two models with AIC-BIC the best model is "MMM" but if I compare with MAPE-MASE the best model is "MAM".

Which model I have to choose and why to choose it ?

I tried to find the best forecasting accuracy but I didn't find anything helpful.

• Please take a look at: stackoverflow.com/help/how-to-ask – mcNets Nov 21 '16 at 19:34
• Have you read the answer? I see you have neither accepted nor upvoted it. Is anything unclear? – Richard Hardy Nov 27 '16 at 16:24
• Thank you Mr.@RichardHardy for your helpful answer. I couldn't reach the web since a week. I convinced that in the same class we have to compare using AIC and BIC. But when comparing forecasting models not in the same class we must use MAPE or MASE. – OmarMH87 Nov 30 '16 at 17:46
• Thanks. Was that a question? If so, why do you think so? – Richard Hardy Nov 30 '16 at 21:11
• Mr. @RichardHardy, I appreciate your help. And the last comment isn't a question. It's an notice I figured it from "Automatic Time Series Forecasting: The forecast Package for R". AIC provides a method for selecting between additive and multiplicative error methods because it is based on likelihood rather than one-step forecasts. – OmarMH87 Dec 2 '16 at 21:46