Measuring forecasting accuracy : the case of judgmental adjustments to SKU-level demand forecasts

By A. Davydenko, Robert Fildes. 2013

198 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Management Science and Operations Research, Evaluation & Benchmarks, Time-Series & Forecasting

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Citations in the corpus

Forecasting with temporal hierarchies (2017, methodology): “The forecasts are evaluated using the Relative Mean Absolute Error (RMAE) (see Davydenko and Fildes, 2013) and the Mean Absolute Scaled Error (MASE) (see Hyndman and Koehler, 2006).”
On the identification of sales forecasting models in the presence of promotions (2015, background): “Particularly, recent studies (Davydenko and Fildes, 2013; Snyder et al., 2012) propose the Average Relative MAE and distribution based scores respectively, to enhance the forecasting accuracy measure in supply chain forecasting applications.”
Tales from tails: On the empirical distributions of forecasting errors and their implication to risk (2019, background): “…is highly inadvisable, relative errors and relative measures are more robust but still inappropriate for many problems, and that scaled errors, as well as median operators of the latter, are probably the best existing alternatives (Hyndman & Koehler, 2006; Davydenko & Fildes, 2013; Fildes, 1992).”
A new accuracy measure based on bounded relative error for time series forecasting (2017, methodology): “However, this measure can still be dominated by a single large error, though infinite and undefined values have been well avoided for most cases [24].”
Statistical models underlying functions of 'smooth' package for R (2017, background): “Literature on error measures is extensive (Fildes, 1992; Hyndman and Koehler, 2006; Davydenko and Fildes, 2013; Kourentzes, 2014; Petropoulos and Kourentzes, 2015), so we are not discussing their properties here;”