We have, however, devised a new approach to forecasting evaluation, a predictive error-forecasting model which compares and evaluates forecasting methods based on their factor levels when faced with intermittent demand.
To tackle this issue, we propose a new measure, CAFE (Cumulative Absolute Forecast Error), to evaluate forecasting methods in terms of total cost.
Evolutionary algorithms have been widely applied in parameter estimation and it appears natural that its application in DA be compared to standard methods, particularly, to evaluate forecasting performance of these methods.
Coefficient of determination, (relative) root mean squared error and Nash-Sutcliffe efficiency were used to evaluate forecasts, the model selection was based on an out-of-sample validation on rolling basis.
Numerous metrics are used in the literature to evaluate forecast accuracy.
A skill score is typically defined in terms of a metric used to evaluate forecast accuracy (e.g., MAE, RMSE, and RMSE%).
Consider including the following verbs, if they apply to your experience and skills: achieved, adapted, analyzed, constructed, coordinated, designed, developed, evaluated, forecasted, generated, improved, increased, launched, measured, negotiated, organized, presented, reported, solved, supported and trained.
It has been argued that the forecasting accuracy of Delphi studies is strongly reliable; for example, a Delphi study with medical doctors evaluating the forecasting application of the method, revealed that in 75% of the cases the estimated values proved to be less than 10% different from the observed [ 26].
