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Some Empirical Findings on Short‐Term Forecasting: Technique Complexity and Combinations
Author(s) -
Sanders Nada R.,
Ritzman Larry P.
Publication year - 1989
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.1989.tb01572.x
Subject(s) - mean absolute percentage error , volatility (finance) , term (time) , forecast error , econometrics , series (stratigraphy) , epitome , statistics , computer science , time series , mean squared error , mathematics , machine learning , paleontology , physics , quantum mechanics , biology
The purpose of this research is to determine if prior findings that favor simple forecasting techniques and technique combinations hold true in a short‐term forecasting environment, where demand data can be quite volatile. Twenty‐two time series of daily data from a real business setting are used to test one‐period ahead forecasts, the epitome of short‐term forecasting. The time series vary systematically as to data volatility and forecast difficulty. Forecast accuracy is measured in terms of both mean absolute percentage error (MAPE) and mean percentage error (MPE).