Financial forecasting using ANFIS networks with Quantum-behaved Particle Swarm Optimization |
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| Affiliation: | 1. Department of Mechanical Engineering, Faculty of Engineering, University of Guilan, Iran;2. Department of Computer and Information Technology, Faculty of Pardis, University of Guilan, Iran;3. Department of Management, Faculty of Literature and Humanities, University of Guilan, Iran;1. Department of Energy, Politecnico di Milano, via Ponzio 34/3, 20133 Milan, Italy;2. Systems Science and the Energetic Challenge, European Foundation for New Energy-Electricité de France, Ecole Centrale Paris and Supelec, Paris, 92295 Chatenay-Malabry Cedex, France;3. Faculty of Engineering and Computing, Coventry University, Priory Street, Coventry, UK;1. Institute of Computing, University of Campinas, SP, Brazil;2. Dept. of Computer Engineering, Federal Technological University of Parana, PR, Brazil;3. IMMUNOCAMP Research and Development of Technology, SP, Brazil;4. Institute of Biology, University of Campinas, SP, Brazil;1. Institute for Development & Research in Banking Technology, Hyderabad, India;2. School of Computer & Information Science, University of Hyderabad, Hyderabad, India;1. Instituto Superior Técnico, Universidade de Lisboa, Av. Prof. Dr. Aníbal Cavaco Silva, 2744-016 Porto Salvo, Portugal;2. INESC-ID Lisboa, Av. Prof. Dr. Aníbal Cavaco Silva, 2744-016 Porto Salvo, Portugal;1. Department of Computer Architecture and Technology, University of the Basque Country UPV/EHU, Donostia-San Sebastian 20018, Spain;2. Department of Computer Science and Artificial Intelligence, University of the Basque Country UPV/EHU, Donostia-San Sebastian 20018, Spain |
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| Abstract: | To be successful in financial market trading it is necessary to correctly predict future market trends. Most professional traders use technical analysis to forecast future market prices. In this paper, we present a new hybrid intelligent method to forecast financial time series, especially for the Foreign Exchange Market (FX). To emulate the way real traders make predictions, this method uses both historical market data and chart patterns to forecast market trends. First, wavelet full decomposition of time series analysis was used as an Adaptive Network-based Fuzzy Inference System (ANFIS) input data for forecasting future market prices. Also, Quantum-behaved Particle Swarm Optimization (QPSO) for tuning the ANFIS membership functions has been used. The second part of this paper proposes a novel hybrid Dynamic Time Warping (DTW)-Wavelet Transform (WT) method for automatic pattern extraction. The results indicate that the presented hybrid method is a very useful and effective one for financial price forecasting and financial pattern extraction. |
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| Keywords: | Financial forecasting Financial pattern recognition Adaptive Network-based Fuzzy Inference System (ANFIS) Quantum-behaved Particle Swarm Optimization (QPSO) Dynamic Time Warping (DTW) Wavelet Transform (WT) |
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