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Fuzzy Time Series Forecasting: A Survey

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Computational Intelligence in Data Mining

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 990))

Abstract

Over the past 25 years, fuzzy time series forecasting (TSF) methods have remained a keen area of interest among the forecasters of different domains. A number of fuzzy TSF methods have been developed and applied in a wide variety of applications. This paper reviews related research papers from the period between 1993 and 2017 with a focus on the development of state of the art. The related studies are compared based on factor and order of model, length of the interval, fuzzy logical relationship (FLR), defuzzification technique, and other experimental factors. This paper also outlines the current achievements, limitations, and suggestions for future research associated with the fuzzy time series forecasting.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, VSSUT, Burla, 768018, Odisha, India

    Sibarama Panigrahi

  2. Department of Information Technology, VSSUT, Burla, 768018, Odisha, India

    Himansu Sekhar Behera

Authors
  1. Sibarama Panigrahi
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  2. Himansu Sekhar Behera
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Correspondence to Sibarama Panigrahi .

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Editors and Affiliations

  1. Department of Information Technology, Veer Surendra Sai University of Technology, Burla, Sambalpur, Odisha, India

    Himansu Sekhar Behera

  2. Department of Computer Science and Engineering, Sri Sivani College of Engineering, Srikakulam, Andhra Pradesh, India

    Janmenjoy Nayak

  3. Department of Computer Application, Veer Surendra Sai University of Technology, Burla, Sambalpur, Odisha, India

    Bighnaraj Naik

  4. Faculty of Communication Sciences, University of Teramo, Teramo, Italy

    Danilo Pelusi

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Panigrahi, S., Behera, H.S. (2020). Fuzzy Time Series Forecasting: A Survey. In: Behera, H., Nayak, J., Naik, B., Pelusi, D. (eds) Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 990. Springer, Singapore. https://doi.org/10.1007/978-981-13-8676-3_54

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