2012 AShapeletTransformforTimeSeries

• (Lines et al., 2012) ⇒ Jason Lines, Luke M. Davis, Jon Hills, and Anthony Bagnall. (2012). “A Shapelet Transform for Time Series Classification.” In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012). ISBN:978-1-4503-1462-6 doi:10.1145/2339530.2339579

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Cited By

• http://scholar.google.com/scholar?q=%222012%22+A+Shapelet+Transform+for+Time+Series+Classification
• http://dl.acm.org/citation.cfm?id=2339530.2339579&preflayout=flat#citedby

Quotes

Author Keywords

• Data mining; filter; shapelet; time series; transformation

Abstract

The problem of time series classification (TSC), where we consider any real-valued ordered data a time series, presents a specific machine learning challenge as the ordering of variables is often crucial in finding the best discriminating features. One of the most promising recent approaches is to find shapelets within a data set. A shapelet is a time series subsequence that is identified as being representative of class membership. The original research in this field embedded the procedure of finding shapelets within a decision tree. We propose disconnecting the process of finding shapelets from the classification algorithm by proposing a shapelet transformation. We describe a means of extracting the k best shapelets from a data set in a single pass, and then use these shapelets to transform data by calculating the distances from a series to each shapelet. We demonstrate that transformation into this new data space can improve classification accuracy, whilst retaining the explanatory power provided by shapelets.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 AShapeletTransformforTimeSeries	Jason Lines Luke M. Davis Jon Hills Anthony Bagnall			A Shapelet Transform for Time Series Classification				10.1145/2339530.2339579		2012