DocumentCode :
3038845
Title :
A Novel Hybrid Intelligent Model for Financial Time Series Forecasting and Its Application
Author :
Wang, Wei ; Zhao, Hong ; Li, Qiang ; Liu, Zhixiong
Author_Institution :
Sch. of Econ., Tianjin Polytech. Univ., Tianjin, China
fYear :
2009
fDate :
24-26 July 2009
Firstpage :
279
Lastpage :
282
Abstract :
Due to the fluctuation and complexity of the financial time series, it is difficult to use any single artificial technique to capture its non-stationary property and accurately describe its moving tendency. So a novel hybrid intelligent forecasting model based on empirical mode decomposition (EMD) and support vector regression (SVR) is proposed. EMD can adaptively decompose the complicated raw data into a finite set of intrinsic mode functions (IMFs) and a residue, which have simpler frequency components and higher correlation. Tendencies of these IMFs and the residue are forecasted by SVR respectively, in which the kernel functions are appropriately chosen according to their different fluctuations. The final forecasting value can be obtained by the sum of these prediction results. Successful forecasting application of Shanghai-securities index demonstrates the feasibility and validity of the presented model.
Keywords :
correlation methods; economic forecasting; financial data processing; functions; learning (artificial intelligence); regression analysis; support vector machines; time series; Shanghai-security index; dataset training; empirical mode decomposition; financial time series forecasting; frequency component; hybrid intelligent model; intrinsic mode function; kernel function; nonstationary property; support vector regression; Artificial intelligence; Artificial neural networks; Economic forecasting; Error correction; Fluctuations; Frequency; Kernel; Predictive models; Risk management; Technology forecasting; empirical mode decomposition; financial time series; hybrid intelligent forecasting; support vector regression;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Business Intelligence and Financial Engineering, 2009. BIFE '09. International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-0-7695-3705-4
Type :
conf
DOI :
10.1109/BIFE.2009.71
Filename :
5208884
Link To Document :
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