Abstract
Shapelets are phase independent subseries that can be used to discriminate between time series. Shapelets have proved to be very effective primitives for time series classification. The two most prominent shapelet based classification algorithms are the shapelet transform (ST) and learned shapelets (LS). One significant difference between these approaches is that ST is data driven, whereas LS searches the entire shapelet space through stochastic gradient descent. The weakness of the former is that full enumeration of possible shapelets is very time consuming. The problem with the latter is that it is very dependent on the initialisation of the shapelets. We propose hybridising the two approaches through a pipeline that includes a time constrained data driven shapelet search which is then passed to a neural network architecture of learned shapelets for tuning. The tuned shapelets are extracted and formed into a transform, which is then classified with a rotation forest. We show that this hybrid approach is significantly better than either approach in isolation, and that the resulting classifier is not significantly worse than a full shapelet search.
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Acknowledgement
This research has been partially supported by the Ministerio de Economía, Industria y Competitividad of Spain (Grant Refs. TIN2017-85887-C2-1-P and TIN2017-90567-REDT) as well as Agence Nationale de la Recherche through MATS project (ANR-18-CE23-0006). D. Guijo-Rubio’s research has been supported by the FPU Predoctoral and Short Placements Programs from Ministerio de Educación y Ciencia of Spain (Grants Ref. FPU16/02128 and EST18/00280, respectively). Some experiments used a Titan X Pascal donated by the NVIDIA Corporation.
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Guijo-Rubio, D., Gutiérrez, P.A., Tavenard, R., Bagnall, A. (2019). A Hybrid Approach to Time Series Classification with Shapelets. In: Yin, H., Camacho, D., Tino, P., Tallón-Ballesteros, A., Menezes, R., Allmendinger, R. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2019. IDEAL 2019. Lecture Notes in Computer Science(), vol 11871. Springer, Cham. https://doi.org/10.1007/978-3-030-33607-3_16
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DOI: https://doi.org/10.1007/978-3-030-33607-3_16
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