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International Journal of Fuzzy Systems

, Volume 20, Issue 3, pp 729–740 | Cite as

A Novel Stochastic Seasonal Fuzzy Time Series Forecasting Model

  • Hilal GuneyEmail author
  • Mehmet Akif Bakir
  • Cagdas Hakan Aladag
Article

Abstract

Fuzzy time series approach has been widely used to analyze real-world time series in recent years since using this approach has some important advantages. Various fuzzy time series models have been proposed in the literature in order to reach better forecasting results. A few of these models have been suggested to forecast seasonal time series and called as seasonal fuzzy time series. In this study, a new seasonal fuzzy time series forecasting model based on Markov chain transition matrix is proposed. In the proposed approach, fuzzy inference process is performed by using transition probabilities. Therefore, fuzzy time series approach proposed in this study is the first stochastic seasonal fuzzy time series method in the literature. To show the forecasting performance of the proposed method, it is applied to two real-world time series: the quarterly U.S. beer production and the number of foreign tourists visiting Turkey. As a result of the implementation, it is observed that the proposed method produces accurate forecasting results for both time series.

Keywords

Forecasting Markov chain Seasonal fuzzy time series Seasonality Transition matrix 

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Copyright information

© Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Hilal Guney
    • 1
    Email author
  • Mehmet Akif Bakir
    • 2
  • Cagdas Hakan Aladag
    • 3
  1. 1.Narman Technical Science CollegeAtatürk UniversityErzurumTurkey
  2. 2.Department of Statistics, Faculty of ScienceGazi UniversityAnkaraTurkey
  3. 3.Department of Statistics, Faculty of ScienceHacettepe UniversityAnkaraTurkey

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