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Fuzzy Time Series Forecasting Method Using Probabilistic Fuzzy Sets

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Advanced Computing and Communication Technologies

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 702))

Abstract

In the literature of time series forecasting, no method can handle both probabilistic and non-probabilistic uncertainty simultaneously. In the current investigation, we have presented probabilistic fuzzy set (PFS) based fuzzy time series (FTS) forecasting model to describe the issue of uncertainties that rises due to randomness as well as linguistic representation of time series data. An aggregation operator is also presented in this paper to aggregate the fuzzified outputs using with membership grades associated with corresponding probabilities. The presented model has been applied to forecast the time series data of University of Alabama enrolments. The performance of presented model has been examined in terms of RMSE and AFE.

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Author information

Authors and Affiliations

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

    Krishna Kumar Gupta & Sanjay Kumar

Authors
  1. Krishna Kumar Gupta
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  2. Sanjay Kumar
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Corresponding author

Correspondence to Sanjay Kumar .

Editor information

Editors and Affiliations

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

    Jyotsna Kumar Mandal

  2. Computational Science Division, Saha Institute of Nuclear Physics, Kolkata, West Bengal, India

    Dhananjay Bhattacharyya

  3. Department of Computer Science and Engineering, Indian Institute of Technology Ropar, Rupnagar, Punjab, India

    Nitin Auluck

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Gupta, K.K., Kumar, S. (2019). Fuzzy Time Series Forecasting Method Using Probabilistic Fuzzy Sets. In: Mandal, J., Bhattacharyya, D., Auluck, N. (eds) Advanced Computing and Communication Technologies. Advances in Intelligent Systems and Computing, vol 702. Springer, Singapore. https://doi.org/10.1007/978-981-13-0680-8_4

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