Journal article
Selecting appropriate forecasting models using rule induction
Omega (Oxford), v 22(6), pp 647-658
1994
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Forecasting is a critical activity for numerous organizations. It is often costly and complex for reasons which include: a multiplicity of forecasting methods and possible combinations; the absence of an overall ‘best’ forecasting method; and the context-dependence of applicable methods, based on available models, data characteristics, and the environment. In recent years, artificial intelligence (AI)-based techniques have been developed to support various operations management activities. This research describes the use of one such AI technique, namely rule induction, to improve forecasting accuracy. Specifically, the proposed methodology involves ‘training’ a rule induction-based expert system (ES) with a set of time series data (the ‘training’ set). Inputs to the ES include selected time series features, and for each time series, the most accurate forecasting method from those available. Subsequently, the ES is used to recommend the most accurate forecasting method for a new set of time series (the ‘testing’ set). The results of this experiment, which appear promising, are presented, together with guidelines for the methodology's use. Its potential benefits include dramatic reductions in the effort and cost of forecasting; the provision of an expert ‘assistant’ for specialist forecasters; and increases in forecasting accuracy.
Metrics
Details
- Title
- Selecting appropriate forecasting models using rule induction
- Creators
- B Arinze - Drexel University
- Publication Details
- Omega (Oxford), v 22(6), pp 647-658
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Decision Sciences (and Management Information Systems)
- Web of Science ID
- WOS:A1994QB14800009
- Scopus ID
- 2-s2.0-0042047333
- Other Identifier
- 991019318934404721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Management
- Operations Research & Management Science