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

DOI10.1007/s10618-020-00701-zzbMath1460.62144arXiv1910.13051OpenAlexW3042807565MaRDI QIDQ2212518

Geoffrey I. Webb, Angus Dempster, François Petitjean

Publication date: 23 November 2020

Published in: Data Mining and Knowledge Discovery (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1910.13051

zbMATH Keywords

scalabilitytime series classificationrandom convolutional kernelsROCKET (RandOm Convolutional KErnel Transform)

Mathematics Subject Classification ID

Random fields (60G60) Computational methods for problems pertaining to statistics (62-08) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Neural nets and related approaches to inference from stochastic processes (62M45) Statistical aspects of big data and data science (62R07)

Related Items (6)

End-to-end deep representation learning for time series clustering: a comparative study ⋮ The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances ⋮ ClaSP: parameter-free time series segmentation ⋮ Time series extrinsic regression. Predicting numeric values from time series data ⋮ rocket ⋮ HIVE-COTE 2.0: a new meta ensemble for time series classification

Uses Software

• Scikit
• PyTorch
• ImageNet
• Numba
• Adam
• TS-CHIEF
• AlexNet
• catch22

Cites Work

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• Deep learning for time series classification: a review
• Classification of time series by shapelet transformation
• catch22
• The BOSS is concerned with time series classification in the presence of noise
• Generalized random shapelet forests
• TS-CHIEF: a scalable and accurate forest algorithm for time series classification
• Interpretable time series classification using linear models and multi-resolution multi-domain symbolic representations
• Should we really use post-hoc tests based on mean-ranks?
• The Singular Value Decomposition: Anatomy of Optimizing an Algorithm for Extreme Scale
• Optimization Methods for Large-Scale Machine Learning

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