2020 ROCKETExceptionallyFastandAccur

• (Dempster et al., 2020) ⇒ Angus Dempster, François Petitjean, and Geoffrey I. Webb. (2020). “ROCKET: Exceptionally Fast and Accurate Time Series Classification Using Random Convolutional Kernels.” In: Data Mining and Knowledge Discovery, 34(5).

Subject Headings: Timeseries Classification, ROCKET System.

Cited By

• http://scholar.google.com/scholar?q=%222020%22+ROCKET%3A+Exceptionally+Fast+and+Accurate+Time+Series+Classification+Using+Random+Convolutional+Kernels

Quotes

Abstract

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2020 ROCKETExceptionallyFastandAccur	Angus Dempster Geoffrey I. Webb François Petitjean			ROCKET: Exceptionally Fast and Accurate Time Series Classification Using Random Convolutional Kernels						2020