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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

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Maritime Economics & Logistics Aims and scope

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.

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Authors and Affiliations

  1. Department of Maritime Logistics, Kobe University, Higashinada, 658-0022, Kobe, Japan

    Emrah Bulut & Shigeru Yoshida

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

    Okan Duru

Authors
  1. Emrah Bulut
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  2. Okan Duru
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  3. Shigeru Yoshida
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Bulut, E., Duru, O. & Yoshida, S. 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. Marit Econ Logist 14, 300–318 (2012). https://doi.org/10.1057/mel.2012.9

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