MAPE, MAD, MSD adalah ukuran untuk membandingkan akurasi antar model dugaan time series, makin kecil nilainya maka model yang digunakan semakin bagus dibandingkan model yang lain

1. Mean absolute percentage error (MAPE) – Expresses accuracy as a percentage of the error. Because this number is a percentage, it may be easier to understand than the other statistics. For example, if the MAPE is 5, on average the forecast is off by 5%.
   
2. Mean absolute deviation (MAD) – Expresses accuracy in the same units as the data, which helps conceptualize the amount of error. Outliers have less of an affect on MAD than on MSD.
   
3. Mean squared deviation (MSD) – A commonly-used measure of accuracy of fitted time series values. Outliers have more influence on MSD than MAD.
   

Pada dua model di atas, yaitu di Single Exponential Smothing (SES) dan Double Exponential Smothing (DES), tampak bahwa MAPE, MAD, dan MSD masing-masing lebih kecil pada DES. Maka model DES lebih tepat daripada SES.

Single Exp Smoothing - di sini dianggap data time seriesnya gak ada tren

Double Exp Smoothing - di sini dianggap data time seriesnya ada tren namun tidak ada pengaruh musiman.

Tentang Single dan Double Smoothing : http://www.uoc.edu/in3/e-math/docs/P...ng_Methods.ppt

Klo Alpha ama Gamma itu nilai Konstanta pada persamaan Model SES ama DES.