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Change point detection for compositional multivariate data

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Abstract

Change point detection algorithms have numerous applications in areas of medical condition monitoring, fault detection in industrial processes, human activity analysis, climate change detection, and speech recognition. We consider the problem of change point detection on compositional multivariate data (each sample is a probability mass function), which is a practically important sub-class of general multivariate data. While the problem of change-point detection is well studied in univariate setting, and there are few viable implementations for a general multivariate data, the existing methods do not perform well on compositional data. In this paper, we propose a parametric approach for change point detection in compositional data. Moreover, using simple transformations on data, we extend our approach to handle any general multivariate data. Experimentally, we show that our method performs significantly better on compositional data and is competitive on general data compared to the available state of the art implementations.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology, Dharwad, India

    Prabuchandran K. J., Pankaj Dayama & Ashutosh Agarwal

  2. IBM Research, Bangalore, India

    Nitin Singh & Vinayaka Pandit

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  1. Prabuchandran K. J.
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  2. Nitin Singh
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  3. Pankaj Dayama
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  4. Ashutosh Agarwal
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  5. Vinayaka Pandit
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Correspondence to Prabuchandran K. J..

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Appendix

Appendix

Table 8 Default Cover scores of various algorithms in different time series (Default setting). Values in bold correspond to the highest value achieved by an algorithm for that time series
Full size table
Table 9 Default F1 scores of various algorithms for different time series (Default setting). Values in bold correspond to the highest value achieved by an algorithm for that time series
Full size table
Table 10 Best Cover scores of various algorithms for different time series (Best setting). Values in bold correspond to the highest value achieved by an algorithm for that time series
Full size table
Table 11 Best F1 scores of various algorithms for different time series (Best setting). Values in bold correspond to the highest value achieved by an algorithm for that time series
Full size table

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K. J., P., Singh, N., Dayama, P. et al. Change point detection for compositional multivariate data. Appl Intell 52, 1930–1955 (2022). https://doi.org/10.1007/s10489-021-02321-6

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