Intermittent demand appears at random, with many time periods having no demand,which is probably the biggest challenge in the repair and overhaul industry. Exponential smoothing is used when dealing with such kind of demand. Based on it, more improved methods have been studied such as Croston method. This paper proposes a novel method to forecast the intermittent parts demand based on fuzzy support vector machines (FSVM) in regression. Details on data clustering, performance criteria design, kernel function selection are presented and an experimental result is given to show the method's validity. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Bao, Y., Zou, H., & Liu, Z. (2006). Forecasting intermittent demand by fuzzy support vector machines. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4031 LNAI, pp. 1080–1089). Springer Verlag. https://doi.org/10.1007/11779568_115
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