Discrete Dynamics in Nature and Society
Volume 2012 (2012), Article ID 593018, 21 pages
http://dx.doi.org/10.1155/2012/593018
Research Article

Multiband Prediction Model for Financial Time Series with Multivariate Empirical Mode Decomposition

Md. Rabiul Islam,1 Md. Rashed-Al-Mahfuz,1 Shamim Ahmad,2 and Md. Khademul Islam Molla2,3

1Department of Computer Science and Engineering, Pabna Science and Technology University, Pabna 6600, Bangladesh
2Department of Computer Science and Engineering, University of Rajshahi, Rajshahi 6205, Bangladesh
3Geophysical Sciences, University of Alberta, Edmonton, AB, Canada T6G 2G7

Received 8 August 2011; Accepted 10 November 2011

Academic Editor: Taher S. Hassan

Copyright © 2012 Md. Rabiul Islam et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper presents a subband approach to financial time series prediction. Multivariate empirical mode decomposition (MEMD) is employed here for multiband representation of multichannel financial time series together. Autoregressive moving average (ARMA) model is used in prediction of individual subband of any time series data. Then all the predicted subband signals are summed up to obtain the overall prediction. The ARMA model works better for stationary signal. With multiband representation, each subband becomes a band-limited (narrow band) signal and hence better prediction is achieved. The performance of the proposed MEMD-ARMA model is compared with classical EMD, discrete wavelet transform (DWT), and with full band ARMA model in terms of signal-to-noise ratio (SNR) and mean square error (MSE) between the original and predicted time series. The simulation results show that the MEMD-ARMA-based method performs better than the other methods.