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ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels

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arxiv 1910.13051 v1 pith:LVIASKFR submitted 2019-10-29 cs.LG stat.ML

ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels

Angus Dempster , Fran\c{c}ois Petitjean , Geoffrey I. Webb This is my paper
classification cs.LG stat.ML
keywords timeclassificationconvolutionalmethodsseriesaccuracycomputationaldatasets
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Most methods for time series classification that attain state-of-the-art accuracy have high computational complexity, requiring significant training time even for smaller datasets, and are intractable for larger datasets. Additionally, many existing methods focus on a single type of feature such as shape or frequency. Building on the recent success of convolutional neural networks for time series classification, we show that simple linear classifiers using random convolutional kernels achieve state-of-the-art accuracy with a fraction of the computational expense of existing methods.

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