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Particle Swarm Optimization and Computational Algorithm Based Weighted Fuzzy Time Series Forecasting Method

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Proceedings of International Joint Conference on Advances in Computational Intelligence

Part of the book series: Algorithms for Intelligent Systems ((AIS))

Abstract

Numerous fuzzy time series (FTS) predictive models had been envisaged in past decades to cope with complicated and undetermined circumstances. The key elements: namely determination of intervals and modeling of fuzzy logical relationships, affect the model’s forecasting accuracy. The manner in which proper fuzzy relationships are generated is pivotal in establishing fuzzy interactions and predictions. Using the prevalent swarm intelligence method of particle swarm optimization (PSO), this work proposes a computational algorithm for forecasting time series by optimizing the weights of fuzzy logical relations (FLRs) of high-order weighted FTS. The relevance of each individual fuzzy relationship in predicting is shown by the weights in FTS. The model’s appropriateness was tested using the University of Alabama enrolment dataset. In the context of average forecasting and root mean square error, the suggested model’s forecasting accuracy was demonstrated to be better than the other models.

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Acknowledgements

The first author gratefully acknowledges the support of the UGC (F. No. 16-9 (June 2018)/2019 (NET/CSIR)) of the Government of India for this research.

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Authors and Affiliations

  1. Department of Mathematics, Statistics and Computer Science, G. B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India

    Shivani Pant & Sanjay Kumar

Authors
  1. Shivani Pant
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  2. Sanjay Kumar
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Corresponding author

Correspondence to Sanjay Kumar .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, Jahangirnagar University, Dhaka, Bangladesh

    Mohammad Shorif Uddin

  2. Nazarbayev University, Nur-Sultan, Kazakhstan

    Prashant Kumar Jamwal

  3. Department of Mathematics, South Asian University, New Delhi, India

    Jagdish Chand Bansal

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Pant, S., Kumar, S. (2022). Particle Swarm Optimization and Computational Algorithm Based Weighted Fuzzy Time Series Forecasting Method. In: Uddin, M.S., Jamwal, P.K., Bansal, J.C. (eds) Proceedings of International Joint Conference on Advances in Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-0332-8_2

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