A new fuzzy functions model tuned by hybridizing imperialist competitive algorithm and simulated annealing. Application: Stock price prediction

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• 作者：M.H. Fazel Zarandi^a ; ^{zarandi@aut.ac.ir} ; M. Zarinbal^a ; N. Ghanbari^a ; I.B. Turksen^b ; ^c
• 关键词：Fuzzy functions ; Noise-rejection possibilistic clustering ; Multivariate adaptive regression splines ; Simulated annealing ; Forecasting
• 刊名：Information Sciences
• 出版年：2013
• 出版时间：10 February, 2013
• 年：2013
• 卷：222
• 期：Complete
• 页码：213-228
• 全文大小：1396 K

文摘

In this paper, a new fuzzy functions (FFs) model is presented and its main parameters are optimized with simulated annealing (SA) approach. For this purpose, a new hybrid clustering algorithm for model structure identification is proposed. This model is based on hybridization of extended version of possibilistic c-mean (PCM) clustering with mahalonobise distance measure and a noise rejection method. In this research, Multivariate Adaptive Regression Splines (MARS) is applied for selecting variables and approximating fuzzy functions in each cluster. A metaheuristic Imperialist Competitive Algorithm (ICA) is used to initialize the clustering parameters. The proposed FFs model is validated using two well-known standard artificial datasets and two real datasets, Tehran stock exchange and ozone level. It is shown that using the proposed FFs model can lead to promising results.