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Fuzzy Approach for Detection of Anomalies in Time Series

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Artificial Intelligence and Soft Computing (ICAISC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11509))

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Abstract

Detecting and removing anomalies in the time series describing physical phenomena is a big challenge faced by scientists from many fields. The aim of the article is to present the assumptions of a tool for the detection of anomalies related to any issue. The proposed universal solution based on fuzzy logic and multicriteria can be applied to any time series. Through the use of aggregation of various approaches, a universal tool based on expert knowledge was obtained. The framework was tested on the energy consumption logs received from a telecommunications company and historical data from the records from the parish registers.

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Author information

Authors and Affiliations

  1. Institute of Computer Science, Lublin University of Technology, ul. Nadbystrzycka 36B, 20-618, Lublin, Poland

    Adam Kiersztyn & Paweł Karczmarek

Authors
  1. Adam Kiersztyn
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  2. Paweł Karczmarek
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Corresponding author

Correspondence to Paweł Karczmarek .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Kiersztyn, A., Karczmarek, P. (2019). Fuzzy Approach for Detection of Anomalies in Time Series. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2019. Lecture Notes in Computer Science(), vol 11509. Springer, Cham. https://doi.org/10.1007/978-3-030-20915-5_36

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  • DOI: https://doi.org/10.1007/978-3-030-20915-5_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20914-8

  • Online ISBN: 978-3-030-20915-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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