Error measures for generalizing about forecasting methods: Empirical comparisons

Error measures for generalizing about forecasting methods: Empirical comparisons

0.00 Avg rating0 Votes
Article ID: iaor1993406
Country: Netherlands
Volume: 8
Start Page Number: 69
End Page Number: 80
Publication Date: Apr 1992
Journal: International Journal of Forecasting
Authors: ,
Abstract:

This study evaluated measures for making comparisons of errors across time series. The authors analyzed 90 annual and 101 quarterly economic time series. They judged error measures on reliability, construct validity, sensitivity to small changes, protection against outliers, and their relationship to decision making. The results lead the authors to recommend the Geometric Mean of the Relative Absolute Error (GMRAE) when the task involves calibrating a model for a set of time series. The GMRAE compares the absolute error of a given method to that from the random walk forecast. For selecting the most accurate methods, the authors recommend the Median RAE (MdRAE) when few series are available and the Median Absolute Percentage Error (MdAPE) otherwise. The Root Mean Square Error (RMSE) is not reliable, and is therefore inappropriate for comparing accuracy across series.

Reviews

Required fields are marked *. Your email address will not be published.