2014 LearningTimeSeriesShapelets

• (Grabocka et al., 2014) ⇒ Josif Grabocka, Nicolas Schilling, Martin Wistuba, and Lars Schmidt-Thieme. (2014). “Learning Time-series Shapelets.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623613

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222014%22+Learning+Time-series+Shapelets
• http://dl.acm.org/citation.cfm?id=2623330.2623613&preflayout=flat#citedby

Quotes

Author Keywords

• Data mining; shapelets; supervised feature extraction; time-series classification

Abstract

Shapelets are discriminative sub-sequences of time series that best predict the target variable. For this reason, shapelet discovery has recently attracted considerable interest within the time-series research community. Currently shapelets are found by evaluating the prediction qualities of numerous candidates extracted from the series segments. In contrast to the state-of-the-art, this paper proposes a novel perspective in terms of learning shapelets. A new mathematical formalization of the task via a classification objective function is proposed and a tailored stochastic gradient learning algorithm is applied. The proposed method enables learning near-to-optimal shapelets directly without the need to try out lots of candidates. Furthermore, our method can learn true top-K shapelets by capturing their interaction. Extensive experimentation demonstrates statistically significant improvement in terms of wins and ranks against 13 baselines over 28 time-series datasets.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 LearningTimeSeriesShapelets	Lars Schmidt-Thieme Josif Grabocka Nicolas Schilling Martin Wistuba			Learning Time-series Shapelets				10.1145/2623330.2623613		2014