NDLI: Wavelet Transform and PSO Support Vector Machine Based Approach for Time Series Forecasting

Content Provider	IEEE Xplore Digital Library
Author	Wang Xiao-Lu Liu Jian Lu Jian-Jun
Copyright Year	2009
Abstract	To accurately predict the non-stationary time series, an approach based on integration of wavelet transform, PSO (Particle Swarm Optimization) and SVM (Support Vector Machine) is proposed. Wavelet decomposition is used to reduce the complexity of time series. Different components are predicted by their corresponding SVM forecasters, respectively, after wavelet transform. The final forecasting result is obtained by combining all predicted results. Taking prediction residual as the fitness value, the parameters of SVM are optimized by a PSO based process. The proposed approach is applied into a coal working face gas concentration forecasting. The results show that simply implanted ANN or SVM based prediction method is not effective when sudden change occurs. The prediction method based on wavelet transform and SVM has better tracking ability and dynamic behavior for suddenly changed data. The performance of the forecaster is remarkably improved to obtain the averaged biases within 3% using the best parameter determined by PSO, which indicates that the suggested approach is feasible and effective.
Starting Page	46
Ending Page	50
File Size	449314
Page Count	5
File Format	PDF
ISBN	9781424438358
DOI	10.1109/AICI.2009.301
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-11-07
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wavelet transforms Wavelet transform Artificial neural networks Prediction methods Time series Predictive models Support Vector Machine Particle swarm optimization Forecasting Support vector machines Forward contracts Risk management Artificial intelligence Computational intelligence Particle Swarm Optimization
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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