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The reality is that modern shipping is an industrial operation comprising a complex web of logistics, thousands upon thousands of transactions, multi-million dollar deals, and billions of dollars' worth of cargo.














































2014 August

Accepted Paper A non-linear clustering method for fuzzy time series: HDP under the optimized cluster paradox New





Paper titled “A non-linear clustering method for fuzzy time series: Histogram damping partition under the optimized cluster paradox” is accepted for publication at Applied Soft Computing (Elsevier BV) and will be available online shortly.


A non-linear clustering method for fuzzy time series: Histogram damping partition under the optimized cluster paradox

Okan Duru, Emrah Bulut



The aim of this paper is to investigate the number of cluster problems in a fuzzy time series. The clustering process has been discussed in the existing literature and a number of methods have been suggested. These methods have several drawbacks, especially the lack of cluster shape and quantity optimization. There are two critical dimensions in a fuzzy time series clustering: the selection of a proper interval for fuzzy clusters and the optimization of the membership degrees among the fuzzy cluster set. The existing methods for the interval selection assume that the intended data has a short-tailed distribution and the cluster intervals are established in identical lengths (e.g. Song and Chissom, 1994; Chen, 1996; Yolcu et al, 2009, among others). However, the time series data (particularly in economic research) is rarely short-tailed and mostly converges to long-tail distribution because of the boom-bust market behavior. This paper proposes a novel clustering method named Histogram Damping Partition (HDP) to define sub-clusters on the standard deviation intervals and truncate the histogram of the data by a constraint based on the coefficient of variation. The HDP approach can be used for many different kinds of fuzzy time series models at the clustering stage.


Keywords: Fuzzy time series, Length of intervals, Cluster optimization.

PACS: 07.05.Mh; 07.05.Kf; 95.75.Wx; 95.75.Pq.

JEL Classification: C100, C110, G000.


Available Online


Applied Soft Computing, Elsevier BV
















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