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

Reza Ebrahimpour, Hossein Nikoo, Saeed Masoudnia, Mohammad Reza Yousefi and Mohammad Sajjad Ghaemi

International Journal of Forecasting, 2011, vol. 27, issue 3, 804-816

Abstract: 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.

Keywords: Modified; stacked; generalization; Mixture; of; MLP; experts; (ME); Adaptive; network-based; fuzzy; inference; system; Combining; neural; networks; Time; series; forecasting (search for similar items in EconPapers)
Date: 2011
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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