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A fuzzy integrated logical forecasting (FILF) model of time charter rates in dry bulk shipping: A vector autoregressive design of fuzzy time series with fuzzy c-means clustering

Author

Listed:
  • Emrah Bulut

    (Department of Maritime Logistics, Kobe University, Higashinada 658-0022, Kobe, Japan. E-mails: bltemrah@gmail.com; syoshida@maritime.kobe-u.ac.jp)

  • Okan Duru

    (Department of Maritime Transportation and Management Engineering, Istanbul Technical University, Tuzla 34940, Istanbul, Turkey.)

  • Shigeru Yoshida

    (Department of Maritime Logistics, Kobe University, Higashinada 658-0022, Kobe, Japan. E-mails: bltemrah@gmail.com; syoshida@maritime.kobe-u.ac.jp)

Abstract

Fuzzy time series (FTS) is a method of making educated guesses by using fuzzy intervals, which correspond to time series clusters. It is also useful for data noise reduction and is based on rule-based forecasting. The aim of this article is to develop a vector autoregressive fuzzy integrated logical forecasting (VAR-FILF) model for time charter rates of Panamax and Handymax bulk carriers. Results are tested by using Chen's FTS method (cFTS), bivariate cFTS (Bi-cFTS) method and conventional time series methods, and the accuracy of the VAR-FILF method is found to be higher than these methods. In addition, the length of intervals affects the forecasting result and the accuracy of forecasting. Therefore, this study proposes the fuzzy C-means clustering method for the structuring of the fuzzy length of intervals of the FTS forecasting process.

Suggested Citation

  • Emrah Bulut & Okan Duru & Shigeru Yoshida, 2012. "A fuzzy integrated logical forecasting (FILF) model of time charter rates in dry bulk shipping: A vector autoregressive design of fuzzy time series with fuzzy c-means clustering," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 14(3), pages 300-318, September.
    • Handle: RePEc:pal:marecl:v:14:y:2012:i:3:p:300-318
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    Cited by:

    1. Zhang, X. & Chen, M.Y. & Wang, M.G. & Ge, Y.E. & Stanley, H.E., 2019. "A novel hybrid approach to Baltic Dry Index forecasting based on a combined dynamic fluctuation network and artificial intelligence method," Applied Mathematics and Computation, Elsevier, vol. 361(C), pages 499-516.

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