Decision forest for multivariate time series analysis
Authors: 
Nine He, Leyang Li, Osamu YoshieAuthors Info & Claims
Nine He, Leyang Li, Osamu YoshieAuthors Info & ClaimsPages 60 - 65
Abstract
Nowadays with time series accounting for an increasingly large fraction of world's supply of data, there has been an explosion of interest in mining time series data. This paper proposes a multivariate time series classification model which is both effective in classifier's accuracy and comprehensibility. It is composed of two stages: a supervised clustering for pattern extraction and soft discretization decision forest. In supervised clustering, some real time series instances from the training dataset will be selected as class dedicated patterns. While in decision forest, the rule induction helps to improve the knowledge acquisition of the classifier. In addition, soft discretization would further improve the accuracy and comprehensibility of the classifier.
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Index Terms
- Decision forest for multivariate time series analysis
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Published In
December 2011
572 pages
ISBN:9781450307840
DOI:10.1145/2095536
Copyright © 2011 ACM.
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Association for Computing Machinery
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Published: 05 December 2011
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MoMM '11
MoMM '11: The 9th International Conference on Advances in Mobile Computing & Multimedia
December 5 - 7, 2011
Ho Chi Minh City, Vietnam
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