Measures of accuracy (time series analysis)

Use these statistics to compare the fits of different forecasting and smoothing methods. Minitab computes three measures of accuracy of the fitted model: MAPE, MAD, and MSD. The three measures are not very informative by themselves, but you can use them to compare the fits obtained by using different methods. For all three measures, smaller values generally indicate a better fitting model.

·    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%.  

·    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.  

·    Mean squared deviation (MSD) – A commonly-used measure of accuracy of fitted time series values. Outliers have more influence on MSD than MAD.

For example, you have sales data for 36 months and you would like to find a prediction model. You try two models: single exponential smoothing (SES) and linear trend, and get the following results:

All three numbers are lower for the linear trend model compared to the single exponential smoothing method; therefore, the linear trend model seems to provide the better fit.