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In this article, we propose a new approach to the classification of multivariate time series. We use a functional approach to data analysis and combine information from raw data and functional derivatives. To provide a comprehensive comparison, we conducted a set of experiments, testing effectiveness on fifteen multivariate time series datasets from a wide variety of application domains. Our experiments show that this new method provides a more accurate classification of the examined datasets.

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Applying Classification Methods for Multivariate Functional Data

  • 会议论文

  • 发表日期：

    2025年

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  • Tomasz Górecki,
  • Miroslaw Krzyśko,
  • Waldemar Wolyński

摘要

In this article, we propose a new approach to the classification of multivariate time series. We use a functional approach to data analysis and combine information from raw data and functional derivatives. To provide a comprehensive comparison, we conducted a set of experiments, testing effectiveness on fifteen multivariate time series datasets from a wide variety of application domains. Our experiments show that this new method provides a more accurate classification of the examined datasets.