Decision support in time series modeling by pattern recognition

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This research is aimed at presenting a new, pattern recognition-based DSS scheme for the time series model identification. The scheme is based on two principles: pattern matching and inductive learning. Pattern matching is used to classify a pattern of the time series into one of the autoregressive moving-average models. The pattern is obtained from the extended sample autocorrelations of the time series. Inductive learning is used to enhance the capability of recognizing input patterns, and linear discriminants are used to discriminate one pattern from the others. To implement the idea, a decision support system named DSSTSM was designed and a prototype was developed on the microcomputer. Experimental results show that the combination of the pattern recognition principles with a DSS can yield a promising solution to the time series modeling. © 1988.
Publisher
Elsevier
Issue Date
1988-06
Language
English
Citation

DECISION SUPPORT SYSTEMS, v.4, no.2, pp.199 - 207

ISSN
0167-9236
URI
http://hdl.handle.net/10203/4963
Appears in Collection
MT-Journal Papers(저널논문)
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