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Erschienen in:
2001 | OriginalPaper | Buchkapitel
Pattern Extraction for Time Series Classification
verfasst von : Pierre Geurts
Erschienen in: Principles of Data Mining and Knowledge Discovery
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
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In this paper, we propose some new tools to allow machine learning classifiers to cope with time series data. We first argue that many time-series classification problems can be solved by detecting and combining local properties or patterns in time series. Then, a technique is proposed to find patterns which are useful for classification. These patterns are combined to build interpretable classification rules. Experiments, carried out on several artificial and real problems, highlight the interest of the approach both in terms of interpretability and accuracy of the induced classifiers.