Journal
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
Volume 17, Issue 2, Pages 137-143Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.engappai.2004.01.001
Keywords
forecasting; radial basis function networks; recursive estimation
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The paper presents some comparative results from different approaches for solving the problem of forecasting irregularly spaced time series, including those representing irregular demand processes in inventory management or production planning problems. The best-known methods for that purpose are based on simple exponential smoothing of the time and magnitude of occurrence of demand. It is shown how supervised neural networks can be used as suitable filtering and forecasting models for many of those processes. In particular, we discuss the identification and estimation issues, as well as the limitations associated to the application of Gaussian radial or elliptical basis function networks. (C) 2004 Elsevier Ltd. All rights reserved.
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