Data-Driven Pattern Identification and Outlier Detection in Time Series

  • Abdolrahman KhoshrouEmail author
  • Eric J. Pauwels
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 858)


We address the problem of data-driven pattern identification and outlier detection in time series. To this end, we use singular value decomposition (SVD) which is a well-known technique to compute a low-rank approximation for an arbitrary matrix. By recasting the time series as a matrix it becomes possible to use SVD to highlight the underlying patterns and periodicities. This is done without the need for specifying user-defined parameters. From a data mining perspective, this opens up new ways of analyzing time series in a data-driven, bottom-up fashion. However, in order to get correct results, it is important to understand how the SVD-spectrum of a time series is influenced by various characteristics of the underlying signal and noise. In this paper, we have extended the work in earlier papers by initiating a more systematic analysis of these effects. We then illustrate our findings on some real-life data.


Data mining Time series Outliers Singular value decomposition (SVD) Parameter-free approximation 



The authors would like to acknowledge partial support by the Dutch TTW-project SES-BE.


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Centrum Wiskunde and InformaticaAmsterdamThe Netherlands

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