Long term dry cargo freight rates forecasting by using recurrent fuzzy neural networks

被引：14

作者：

Uyar, Kaan ^{[1
]}

Ilhan, Umit ^{[1
]}

Ilhan, Ahmet ^{[1
]}

机构：

[1] Near East Univ, Dept Comp Engn, POB 99138, TR-10 North Cyprus, Mersin, Turkey

来源：

12TH INTERNATIONAL CONFERENCE ON APPLICATION OF FUZZY SYSTEMS AND SOFT COMPUTING, ICAFS 2016 | 2016年 / 102卷

关键词：

Forecasting; genetic algorithm; recurrent fuzzy neural networks; TIME-SERIES; MARKET; MODEL;

D O I：

10.1016/j.procs.2016.09.455

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Maritime transport rates are very important for planning economic strategies. Various methods have been applied to seaborne trade forecasting. This study presents a genetic algorithm based trained recurrent fuzzy neural network for long term dry cargo freight rates forecasting. The empirical results show that proposed work has better accuracy than the other approaches which have used the same data set. (C) 2016 The Authors. Published by Elsevier B.V.

引用

页码：642 / 647

页数：6

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