Multiband Prediction Model for Financial Time Series with Multivariate Empirical Mode Decomposition
نویسندگان
چکیده
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. Autoregressivemoving 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 proposedMEMD-ARMAmodel is compared with classical EMD, discrete wavelet transform DWT , and with full band ARMAmodel 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.
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