Mixture of MLP-experts for trend forecasting of time series: A case study of the Tehran stock exchange

作     者：Ebrahimpour, Reza Nikoo, Hossein Masoudnia, Saeed Yousefi, Mohammad Reza Ghaemi, Mohammad Sajjad 

作者机构：Inst Res Fundamental Sci Sch Cognit Sci Niavaran Iran Shahid Rajaee Univ Dept Elect & Comp Engn Tehran Iran Islamic Azad Univ Qazvin Management & Accounting Fac Qazvin Iran Univ Tehran Dept Math Stat & Comp Sci Tehran 14174 Iran Int Max Planck Res Sch Comp Sci Saarbrucken Germany Sharif Univ Technol Dept Math Sci Tehran Iran RCISP Tehran Iran 

出 版 物：《INTERNATIONAL JOURNAL OF FORECASTING》 (国际预测杂志)

年 卷 期：2011年第27卷第3期

页      面：804-816页

核心收录：

学科分类：0201[经济学-理论经济学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 08[工学] 

主　　题：Modified stacked generalization Mixture of MLP experts (ME) Adaptive network-based fuzzy inference system Combining neural networks Time series forecasting 

摘      要：A new method for forecasting the trend of time series, based on mixture of MLP experts, is presented. In this paper, three neural network combining methods and an Adaptive Network-Based Fuzzy Inference System (ANFIS) are applied to trend forecasting in the Tehran stock exchange. There are two experiments in this study. In experiment I, the time series data are the Kharg petrochemical company s daily closing prices on the Tehran stock exchange. In this case study, which considers different schemes for forecasting the trend of the time series, the recognition rates are 75.97%, 77.13% and 81.64% for stacked generalization, modified stacked generalization and ANFIS, respectively. Using the mixture of MLP experts (ME) scheme, the recognition rate is strongly increased to 86.35%. A gain and loss analysis is also used, showing the relative forecasting success of the ME method with and without rejection criteria, compared to a simple buy and hold approach. In experiment II, the time series data are the daily closing prices of 37 companies on the Tehran stock exchange. This experiment is conducted to verify the results of experiment I and to show the efficiency of the ME method compared to stacked generalization, modified stacked generalization and ANFIS. (C) 2010 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.