2009 TimeSeriesShapeletsaNewPrimitiv

Subject Headings:

Notes

Categories and Subject Descriptors: H.2.8 Database Management: Database Applications – Data Mining.
General Terms: Algorithms, Experimentation

Classification of time series has been attracting great interest over the past decade. Recent empirical evidence has strongly suggested that the simple nearest neighbor algorithm is very difficult to beat for most time series problems. While this may be considered good news, given the simplicity of implementing the nearest neighbor algorithm, there are some negative consequences of this. First, the nearest neighbor algorithm requires storing and searching the entire dataset, resulting in an time and space complexity that limits its applicability, especially on resource-limited sensors. Second, beyond mere classification accuracy, we often wish to gain some insight into the data. In this work we introduce a new time series primitive, time series shapelets, which addresses these limitations. Informally, shapelets are time series subsequences which are in some sense maximally representative of a class. As we shall show with extensive empirical evaluations in diverse domains, algorithms based on the time series shapelet primitives can be interpretable, more accurate and significantly faster than state-of-the-art classifiers

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 TimeSeriesShapeletsaNewPrimitiv	Eamonn Keogh Lexiang Ye			Time Series Shapelets: A New Primitive for Data Mining		KDD-2009 Proceedings		10.1145/1557019.1557122		2009