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Towards Symbolic Time Series Representation Improved by Kernel Density Estimators

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Transactions on Large-Scale Data- and Knowledge-Centered Systems L

Part of the book series: Lecture Notes in Computer Science ((TLDKS,volume 12930))

Abstract

This paper deals with symbolic time series representation. It builds up on the popular mapping technique Symbolic Aggregate approXimation algorithm (SAX), which is extensively utilized in sequence classification, pattern mining, anomaly detection, time series indexing and other data mining tasks. However, the disadvantage of this method is, that it works reliably only for time series with Gaussian-like distribution. In our previous work (Kloska and Rozinajova, dwSAX, 2020) we have proposed an improvement of SAX, called dwSAX, which can deal with Gaussian as well as non-Gaussian data distribution. Recently we have made further progress in our solution - edwSAX. Our goal was to optimally cover the information space by means of sufficient alphabet utilization; and to satisfy lower bounding criterion as tight as possible. We describe here our approach, including evaluation on commonly employed tasks such as time series reconstruction error and Euclidean distance lower bounding with promising improvements over SAX.

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Acknowledgement

This research was supported by TAILOR, a project funded by Horizon 2020 research and innovation programme under GA no 952215 and “Knowledge-based Approach to Intelligent Big Data Analysis” - Slovak Research and Development Agency under the contract No. APVV-16-0213.

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Authors and Affiliations

  1. Faculty of Informatics and Information Technologies, Slovak University of Technology in Bratislava, Ilkovičova 2, 842 16, Bratislava, Slovakia

    Matej Kloska

  2. Kempelen Institute of Intelligent Technologies, Mlynské Nivy II.18890/5, 811 09, Bratislava, Slovakia

    Viera Rozinajova

Authors
  1. Matej Kloska
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  2. Viera Rozinajova
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Correspondence to Matej Kloska .

Editor information

Editors and Affiliations

  1. IRIT, Paul Sabatier University, Toulouse, France

    Abdelkader Hameurlain

  2. IFS, Technical University of Vienna, Vienna, Austria

    A Min Tjoa

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Kloska, M., Rozinajova, V. (2021). Towards Symbolic Time Series Representation Improved by Kernel Density Estimators. In: Hameurlain, A., Tjoa, A.M. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems L. Lecture Notes in Computer Science(), vol 12930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-64553-6_2

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  • DOI: https://doi.org/10.1007/978-3-662-64553-6_2

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  • Online ISBN: 978-3-662-64553-6

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