A PSO based integrated functional link net and interval type-2 fuzzy logic system for predicting stock market indices

This paper presents an integrated functional link interval type-2 fuzzy neural system (FLIT2FNS) for predicting the stock market indices. The hybrid model uses a TSK (Takagi–Sugano–Kang) type fuzzy rule base that employs type-2 fuzzy sets in the antecedent parts and the outputs from the Functional L...

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Veröffentlicht in:Applied soft computing Jg. 12; H. 2; S. 931 - 941
Hauptverfasser: Chakravarty, S., Dash, P.K.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.02.2012
Schlagworte:
Backpropagation learning algorithm
Functional Link Artificial Neural Network
Fuzzy logic system
Interval Type-2FLS
Particle swarm optimization
Functional Link Artificial Neural Network
Interval Type-2FLS
Backpropagation learning algorithm
Particle swarm optimization
Fuzzy logic system
ISSN:1568-4946, 1872-9681
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Zusammenfassung:This paper presents an integrated functional link interval type-2 fuzzy neural system (FLIT2FNS) for predicting the stock market indices. The hybrid model uses a TSK (Takagi–Sugano–Kang) type fuzzy rule base that employs type-2 fuzzy sets in the antecedent parts and the outputs from the Functional Link Artificial Neural Network (FLANN) in the consequent parts. Two other approaches, namely the integrated FLANN and type-1 fuzzy logic system and Local Linear Wavelet Neural Network (LLWNN) are also presented for a comparative study. Backpropagation and particle swarm optimization (PSO) learning algorithms have been used independently to optimize the parameters of all the forecasting models. To test the model performance, three well known stock market indices like the Standard's & Poor's 500 (S&P 500), Bombay stock exchange (BSE), and Dow Jones industrial average (DJIA) are used. The mean absolute percentage error (MAPE) and root mean square error (RMSE) are used to find out the performance of all the three models. Finally, it is observed that out of three methods, FLIT2FNS performs the best irrespective of the time horizons spanning from 1 day to 1 month.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2011.09.013