A Neural Network System for Forecasting Method Selection

Publication Type

Journal Article

Publication Date

1994

Abstract

Choosing an appropriate forecasting method is a crucial decision for most organizations, as the company's success is highly dependent on the accurate prediction of future. The decision, however, is not easy because many forecasting methods are available and the selection often requires extensive statistical knowledge, and personal judgment. In this paper, we illustrate how can a neural network approach be used to ease this task. We first examine the general technical issues (decisions) involved in designing neural network applications. A backpropagation-based forecasting prototype is then used to demonstrate how these decisions be determined in practice.

Keywords

Neural networks, Forecasting method selection, Backpropagation, Exponential smoothing, Forecasting

Discipline

Computer Sciences | Management Information Systems

Research Areas

Information Systems and Management

Publication

Decision Support Systems

Volume

12

Issue

1

First Page

13

Last Page

24

ISSN

0167-9236

Identifier

10.1016/0167-9236(94)90071-X

Publisher

Elsevier

Additional URL

http://dx.doi.org/10.1016/0167-9236(94)90071-X

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