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Grey relational with BP_PSO for time series foreasting

Shamsuddin, Siti Mariyam and Sallehudin, Roselina (2009) Grey relational with BP_PSO for time series foreasting. In: 2009 IEEE International Conference on Systems, Man and Cybernatics (SMC 2009) , 2009, Texas, Amerika Syarikat.

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Official URL: http://dx.doi.org/10.1109/ICSMC.2009.5346304

Abstract

This paper proposes an effective hybridization of grey relational analysis (GRA) and Backpropagation Particle Swarm Optimization (BP_PSO) for time series forecasting. The hybridization employs the complementary strength of these two appealing techniques. Additionally the combination of GRA and BP as cooperative feature selection (CFS) has successfully assessed the importance of each input variable and automatically suggest the optimum input numbers for the forecasting task. Therefore it assists the forecaster to choose the optimum number of dominant input factor without a need to acquire expert domain knowledge. It also helps to reduce the interference of irrelevant inputs on the forecasting accuracy performance. To test the effectiveness of the proposed hybrid GRABP_PSO, the dataset of closing price from Kuala Lumpur Stock Exchange (KLSE) is used. The results show that the proposed model, GRBP_PSO out performed BP_PSO model and BP model in term of accuracy and convergence time.

Item Type:Conference or Workshop Item (Paper)
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computer Science and Information System (Formerly known)
ID Code:15248
Deposited By: Liza Porijo
Deposited On:22 Sep 2011 09:50
Last Modified:22 Sep 2011 09:50

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