ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels. 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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References in zbMATH (referenced in 5 articles , 1 standard article )
Showing results 1 to 5 of 5.
- Julien Siebert, Janek Groß, Christof Schroth: A systematic review of Python packages for time series analysis (2021) arXiv
- Middlehurst, Matthew; Large, James; Flynn, Michael; Lines, Jason; Bostrom, Aaron; Bagnall, Anthony: HIVE-COTE 2.0: a new meta ensemble for time series classification (2021)
- Pasos Ruiz, Alejandro; Flynn, Michael; Large, James; Middlehurst, Matthew; Bagnall, Anthony: The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances (2021)
- Tan, Chang Wei; Bergmeir, Christoph; Petitjean, François; Webb, Geoffrey I.: Time series extrinsic regression. Predicting numeric values from time series data (2021)
- Dempster, Angus; Petitjean, François; Webb, Geoffrey I.: ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels (2020)