MultiRocket: Effective summary statistics for convolutional outputs in time series classification

Rocket and MiniRocket, while two of the fastest methods for time series classification, are both somewhat less accurate than the current most accurate methods (namely, HIVE-COTE and its variants). We show that it is possible to significantly improve the accuracy of MiniRocket (and Rocket), with some additional computational expense, by expanding the set of features produced by the transform, making MultiRocket (for MiniRocket with Multiple Features) overall the single most accurate method on the datasets in the UCR archive, while still being orders of magnitude faster than any algorithm of comparable accuracy other than its precursors.

[1]  N. Jones,et al.  hctsa: A Computational Framework for Automated Time-Series Phenotyping Using Massive Feature Extraction. , 2017, Cell systems.

[2]  Jason Lines,et al.  Time series classification with ensembles of elastic distance measures , 2015, Data Mining and Knowledge Discovery.

[3]  Geoffrey I. Webb,et al.  InceptionTime: Finding AlexNet for time series classification , 2019, Data Mining and Knowledge Discovery.

[4]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[5]  Germain Forestier,et al.  Deep learning for time series classification: a review , 2018, Data Mining and Knowledge Discovery.

[6]  Geoffrey I. Webb,et al.  ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels , 2019, Data Mining and Knowledge Discovery.

[7]  Eamonn J. Keogh,et al.  The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances , 2016, Data Mining and Knowledge Discovery.

[8]  Eamonn J. Keogh,et al.  The UCR time series archive , 2018, IEEE/CAA Journal of Automatica Sinica.

[9]  Geoffrey I. Webb,et al.  TS-CHIEF: a scalable and accurate forest algorithm for time series classification , 2019, Data Mining and Knowledge Discovery.

[10]  Geoffrey I. Webb,et al.  Time series extrinsic regression , 2020, Data Mining and Knowledge Discovery.

[11]  Patrick Schäfer,et al.  Scalable time series classification , 2016, Data Mining and Knowledge Discovery.

[12]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[13]  Geoffrey I. Webb,et al.  Proximity Forest: an effective and scalable distance-based classifier for time series , 2018, Data Mining and Knowledge Discovery.

[14]  George C. Runger,et al.  A time series forest for classification and feature extraction , 2013, Inf. Sci..

[15]  Geoffrey I. Webb,et al.  MiniRocket: A Very Fast (Almost) Deterministic Transform for Time Series Classification , 2020, KDD.

[16]  Siu Kwan Lam,et al.  Numba: a LLVM-based Python JIT compiler , 2015, LLVM '15.

[17]  James Large,et al.  On the Usage and Performance of the Hierarchical Vote Collective of Transformation-Based Ensembles Version 1.0 (HIVE-COTE v1.0) , 2020, AALTD@PKDD/ECML.

[18]  Jason Lines,et al.  Classification of time series by shapelet transformation , 2013, Data Mining and Knowledge Discovery.