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

Author

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  • Md. Rabiul Islam
  • Md. Rashed-Al-Mahfuz
  • Shamim Ahmad
  • Md. Khademul Islam Molla

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.

Suggested Citation

  • Md. Rabiul Islam & Md. Rashed-Al-Mahfuz & Shamim Ahmad & Md. Khademul Islam Molla, 2012. "Multiband Prediction Model for Financial Time Series with Multivariate Empirical Mode Decomposition," Discrete Dynamics in Nature and Society, Hindawi, vol. 2012, pages 1-21, March.
    • Handle: RePEc:hin:jnddns:593018
    DOI: 10.1155/2012/593018
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    Cited by:

    1. Ganggang Guo & Yulei Rao & Feida Zhu & Fang Xu, 2020. "Innovative deep matching algorithm for stock portfolio selection using deep stock profiles," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-31, November.
    2. Lin, Ling & Jiang, Yong & Xiao, Helu & Zhou, Zhongbao, 2020. "Crude oil price forecasting based on a novel hybrid long memory GARCH-M and wavelet analysis model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 543(C).
    3. Tang, Ling & Zhang, Chengyuan & Li, Ling & Wang, Shouyang, 2020. "A multi-scale method for forecasting oil price with multi-factor search engine data," Applied Energy, Elsevier, vol. 257(C).

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