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An Improved Technique for Modeling Fuzzy Time Series

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Computational Intelligence, Communications, and Business Analytics (CICBA 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1030))

Abstract

Fuzzy time series forecasting emerged as a new field of study during 1990 s. In recent years it has become a popular research topic. In Fuzzy time series forecasting, performance of the model depends on two important steps: (1) Interval creation technique and (2) determination of fuzzy logical relationships (FLR) between past and present state. This paper presents a variable length data partitioning technique using Particle Swarm Optimization (PSO). Prediction is done by computing magnitude and direction of transition by simple mathematical operations. Novelty of this model is that it considers only the upward and the downward transitions while computing net variation. It does not take into consideration those cases where there is no change of states. In the iterative procedure, depending upon the position of cut points, lower bound and upper bound of the intervals (on which fuzzy sets are defined) are changed. This will in turn change the mid points of the interval which is used to measure variation of transition. This procedure is repeated until optimum positions of cuts corresponding to the lowest forecasting error are obtained. The experiment is carried out on standard datasets and results are compared with related models applied on the same dataset. Proposed idea outperforms others in terms of accuracy in prediction.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Kalyani, Kalyani, India

    Mahua Bose & Kalyani Mali

Authors
  1. Mahua Bose
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  2. Kalyani Mali
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Corresponding author

Correspondence to Mahua Bose .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India

    Jyotsna Kumar Mandal

  2. Department of Computer Science and Engineering, Assam University, Silchar, Assam, India

    Somnath Mukhopadhyay

  3. Department of Computer and Systems Sciences, Visva Bharati University, Santiniketan, West Bengal, India

    Paramartha Dutta

  4. Department of Computer Science and Engineering, Kalyani Government Engineering College, Kalyani, West Bengal, India

    Kousik Dasgupta

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Bose, M., Mali, K. (2019). An Improved Technique for Modeling Fuzzy Time Series. In: Mandal, J., Mukhopadhyay, S., Dutta, P., Dasgupta, K. (eds) Computational Intelligence, Communications, and Business Analytics. CICBA 2018. Communications in Computer and Information Science, vol 1030. Springer, Singapore. https://doi.org/10.1007/978-981-13-8578-0_10

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  • DOI: https://doi.org/10.1007/978-981-13-8578-0_10

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-8577-3

  • Online ISBN: 978-981-13-8578-0

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