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How Mining and Summarizing Information on Time Series Can Be Formed Using Fuzzy Modeling Methods

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Intelligent and Fuzzy Systems (INFUS 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 504))

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Abstract

In this paper we provide an overview of fuzzy modeling methods applied to time series processing. The basic methods are Fuzzy Transform (F-transform) and selected methods of Fuzzy Natural Logic (FNL). We address classical tasks such as estimation of trend and its prediction, and also methods for mining information from time series. We provide information that can hardly be obtained using statistics. Namely, we automatically form an explanation of the forecast in natural language, provide comments to the slope of time series in an imprecisely specified area, detect possible structural breaks, “bull and bear” phases of financial time series, measure of similarity between time series and provide automatic summarization of knowledge about time series expressed in natural language.

The work was supported from ERDF/ESF by the project “Centre for the development of Artificial Intelligence Methods for the Automotive Industry of the region” No. CZ.02.1.01/0.0/0.0/17-049/0008414.

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Notes

  1. 1.

    The interval [0, 1] is a set of truth values where 0 means falsity, 1 truth and the other values express partial truth. This interval can be replaced by a suitable bounded lattice.

  2. 2.

    In fact, the F-transform provides weighted average of arbitrary derivative but in time series processing we need only the first and second ones.

  3. 3.

    Such a function is implemented in the experimental software LFL Controller and LFL Forecaster (see http://irafm.osu.cz/en/c100_0) developed in the Institute for Research and Applications of Fuzzy Modeling of the University of Ostrava, Czech Republic. The authors are Vilém Novák, Antonín Dvořák and Viktor Pavliska. The results demonstrated in this paper were obtained using the mentioned software.

  4. 4.

    For example, the context for height of trees in Europe can be \(v_L=1\) m, \(v_R=50\) m and \(v_S=20\) m.

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Authors and Affiliations

  1. Institute for Research and Applications of Fuzzy Modeling, University of Ostrava, 30. dubna 22, 701 03, Ostrava 1, Czech Republic

    Vilém Novák

Authors
  1. Vilém Novák
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Correspondence to Vilém Novák .

Editor information

Editors and Affiliations

  1. Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey

    Cengiz Kahraman

  2. Department of Industrial Engineering, Galatasaray University, Istanbul, Turkey

    A. Cagri Tolga

  3. Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey

    Sezi Cevik Onar

  4. Department of Industrial Engineering, Yildiz Technical University, Istanbul, Turkey

    Selcuk Cebi

  5. Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey

    Basar Oztaysi

  6. Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey

    Irem Ucal Sari

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Novák, V. (2022). How Mining and Summarizing Information on Time Series Can Be Formed Using Fuzzy Modeling Methods. In: Kahraman, C., Tolga, A.C., Cevik Onar, S., Cebi, S., Oztaysi, B., Sari, I.U. (eds) Intelligent and Fuzzy Systems. INFUS 2022. Lecture Notes in Networks and Systems, vol 504. Springer, Cham. https://doi.org/10.1007/978-3-031-09173-5_7

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