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TS-CHIEF: a scalable and accurate forest algorithm for time series classification

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Abstract

Time Series Classification (TSC) has seen enormous progress over the last two decades. HIVE-COTE (Hierarchical Vote Collective of Transformation-based Ensembles) is the current state of the art in terms of classification accuracy. HIVE-COTE recognizes that time series data are a specific data type for which the traditional attribute-value representation, used predominantly in machine learning, fails to provide a relevant representation. HIVE-COTE combines multiple types of classifiers: each extracting information about a specific aspect of a time series, be it in the time domain, frequency domain or summarization of intervals within the series. However, HIVE-COTE (and its predecessor, FLAT-COTE) is often infeasible to run on even modest amounts of data. For instance, training HIVE-COTE on a dataset with only 1500 time series can require 8 days of CPU time. It has polynomial runtime with respect to the training set size, so this problem compounds as data quantity increases. We propose a novel TSC algorithm, TS-CHIEF (Time Series Combination of Heterogeneous and Integrated Embedding Forest), which rivals HIVE-COTE in accuracy but requires only a fraction of the runtime. TS-CHIEF constructs an ensemble classifier that integrates the most effective embeddings of time series that research has developed in the last decade. It uses tree-structured classifiers to do so efficiently. We assess TS-CHIEF on 85 datasets of the University of California Riverside (UCR) archive, where it achieves state-of-the-art accuracy with scalability and efficiency. We demonstrate that TS-CHIEF can be trained on 130 k time series in 2 days, a data quantity that is beyond the reach of any TSC algorithm with comparable accuracy.

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Acknowledgements

This research was supported by the Australian Research Council under grant DE170100037. This material is based upon work supported by the Air Force Office of Scientific Research, Asian Office of Aerospace Research and Development (AOARD) under award number FA2386-17-1-4036. The authors would like to thank Prof. Eamonn Keogh and all the people who have contributed to the UCR time series classification archive. We also would like to acknowledge the use of source code freely available at http://www.timeseriesclassification.com and thank Prof. Anthony Bagnall and other contributors of the project. We also acknowledge the use of source code freely provided by the original author of BOSS algorithm, Dr. Patrick Schäfer. Finally, we acknowledge the use of two Java libraries (Osinski and Weiss 2015; Friedman and Eden 2013), which was used to optimize the implementation of our source code.

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Authors and Affiliations

  1. Faculty of Information Technology, Monash University, 25 Exhibition Walk, 3800, Melbourne, VIC, Australia

    Ahmed Shifaz, Charlotte Pelletier, François Petitjean & Geoffrey I. Webb

  2. IRISA, UMR CNRS 6074, University of Bretagne Sud, Campus de Tohannic, BP 573, 56 000, Vannes, France

    Charlotte Pelletier

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  1. Ahmed Shifaz
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Correspondence to Ahmed Shifaz.

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Appendix

Appendix

See Tables 3 and 4.

Table 3 Complexities of the methods mentioned in Sect. 2
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Table 4 Accuracy of leading TSC classifiers on 85 UCR datasets
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Shifaz, A., Pelletier, C., Petitjean, F. et al. TS-CHIEF: a scalable and accurate forest algorithm for time series classification. Data Min Knowl Disc 34, 742–775 (2020). https://doi.org/10.1007/s10618-020-00679-8

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