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A review on distance based time series classification

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Abstract

Time series classification is an increasing research topic due to the vast amount of time series data that is being created over a wide variety of fields. The particularity of the data makes it a challenging task and different approaches have been taken, including the distance based approach. 1-NN has been a widely used method within distance based time series classification due to its simplicity but still good performance. However, its supremacy may be attributed to being able to use specific distances for time series within the classification process and not to the classifier itself. With the aim of exploiting these distances within more complex classifiers, new approaches have arisen in the past few years that are competitive or which outperform the 1-NN based approaches. In some cases, these new methods use the distance measure to transform the series into feature vectors, bridging the gap between time series and traditional classifiers. In other cases, the distances are employed to obtain a time series kernel and enable the use of kernel methods for time series classification. One of the main challenges is that a kernel function must be positive semi-definite, a matter that is also addressed within this review. The presented review includes a taxonomy of all those methods that aim to classify time series using a distance based approach, as well as a discussion of the strengths and weaknesses of each method.

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Notes

  1. UCR is a repository of time series datasets (Chen et al. 2015a) which is often used as a benchmark for evaluating time series classification methods. These datasets are greatly varied with respect to their application domains, time series lengths, number of classes, and sizes of the training and testing sets.

  2. On-line handwritten digit data set (Guyon et al. 1994).

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Acknowledgements

The authors would like to thank the people who contributed to the UCR time series repository, as well as would like to express our sincere appreciation for the comments and advices provided by Eamonn Keogh and Lingfei Wu to improve this paper. This research is supported by the Basque Government through the BERC 2018-2021 program and by Spanish Ministry of Economy and Competitiveness MINECO through BCAM Severo Ochoa excellence accreditation SEV-2013-0323 and through project TIN2017-82626-R funded by (AEI/FEDER, UE) and acronym GECECPAST. In addition, by the Research Groups 2013-2018 (IT-609-13) programs (Basque Government), TIN2016-78365-R (Spanish Ministry of Economy, Industry and Competitiveness). A. Abanda is also supported by the Grant BES-2016-076890.

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  1. Basque Center for Applied Mathematics (BCAM), Mazarredo Zumarkalea, 14, 48009, Bilbao, Spain

    Amaia Abanda & Jose A. Lozano

  2. Intelligent Systems Group (ISG), Department of Computer Science and Artificial Intelligence, University of the Basque Country UPV/EHU, Manuel de Lardizabal 1, 20018, Donostia-San Sebastian, Spain

    Amaia Abanda, Usue Mori & Jose A. Lozano

  3. Department of Applied Mathematics, Statistics and Operational Research, University of the Basque Country UPV/EHU, Barrio Sarriena, s/n, 48940, Leoia, Spain

    Usue Mori

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Abanda, A., Mori, U. & Lozano, J.A. A review on distance based time series classification. Data Min Knowl Disc 33, 378–412 (2019). https://doi.org/10.1007/s10618-018-0596-4

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