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Computational Method for High-Order Weighted Fuzzy Time Series Forecasting Based on Multiple Partitions

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Facets of Uncertainties and Applications

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 125))

Abstract

In this paper, we present modified version of computational algorithm given by Gangwar and Kumar (Expert Syst Appl 39:12158–12164, 2012 [5]) for higher order weighted fuzzy time series with multiple partitioning to enhance the accuracy in forecasting. The developed method provides a better approach to enhance the accuracy in forecasted values. The proposed method was implemented on the historical student enrollments data of University of Alabama. The suitability of the developed method has been examined in comparison with other models in terms of mean square and average forecasting errors to show its superiority.

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Author information

Authors and Affiliations

  1. Department of Mathematics, Statistics & Computer Science, G. B. Pant University of Agriculture & Technology, Pantnagar, 263145, Uttarakhand, India

    Sukhdev Singh Gangwar & Sanjay Kumar

Authors
  1. Sukhdev Singh Gangwar
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  2. Sanjay Kumar
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Corresponding author

Correspondence to Sanjay Kumar .

Editor information

Editors and Affiliations

  1. Jadavpur University, Kolkata, India

    Mihir K. Chakraborty

  2. Institute of Mathematics, University of Warsaw, Warszawa, Poland

    Andrzej Skowron

  3. Vidyasagar University, Paschim Medinipur, West Bengal, India

    Manoranjan Maiti

  4. Department of Mathematics, National Institute of Technology (NIT), Durgapur, India

    Samarjit Kar

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Gangwar, S.S., Kumar, S. (2015). Computational Method for High-Order Weighted Fuzzy Time Series Forecasting Based on Multiple Partitions. In: Chakraborty, M.K., Skowron, A., Maiti, M., Kar, S. (eds) Facets of Uncertainties and Applications. Springer Proceedings in Mathematics & Statistics, vol 125. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2301-6_22

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