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SELECTING PARAMETERS FOR SHORT‐TERM FORECASTING TECHNIQUES
Author(s) -
Dalrymple Douglas J.,
King Barry E.
Publication year - 1981
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.1540-5915.1981.tb00117.x
Subject(s) - term (time) , computer science , technology forecasting , probabilistic forecasting , sales forecasting , econometrics , operations research , artificial intelligence , economics , mathematics , physics , quantum mechanics , probabilistic logic
The objective of this paper is to discover which of three forecasting modes used to select parameters for four short‐term forecasting techniques minimizes errors. The study also examines whether the amount of historical data used to find parameters contributes to forecasting success. The results show the traditional one‐ahead search routine works well in some, but not all, forecasting situations. Also, forecasting errors appear to decline when more historical data are included in the parameter search.