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
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.
Volume (Year): 14 (2012)
Issue (Month): 3 (September)
|Contact details of provider:|| Web page: http://www.palgrave-journals.com/|
|Order Information:|| Postal: Palgrave Macmillan Journals, Subscription Department, Houndmills, Basingstoke, Hampshire RG21 6XS, UK|
Web: http://www.palgrave-journals.com/pal/subscribe/index.html Email:
When requesting a correction, please mention this item's handle: RePEc:pal:marecl:v:14:y:2012:i:3:p:300-318. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Daniel Foley)
If references are entirely missing, you can add them using this form.