Wavelet decomposition for time series: Determining input model by using mRMR criterion

Year 2015, Volume: 44 Issue: 1, 229 - 238, 01.02.2015

Budi Warsito Subanar Subanar Abdurakhman Abdurakhman

Abstract

Determining the level of decomposition and coefficients used as input in
the wavelet modeling for time series has become an interesting problem
in recent years. In this paper, the detail and scaling coefficients that
would be candidates of input determined based on the value of Mutual
Information. Coefficients generated through decomposition with Maximal Overlap Discrete Wavelet Transform (MODWT) were sorted by
Minimal Redundancy Maximal Relevance (mRMR) criteria, then they
were performed using an input modeling that had the largest value
of Mutual Information in order to obtain the predicted value and the
residual of the initial (unrestricted) model. Input was then added one
based on the ranking of mRMR. If additional input no longer produced
a significant decrease of the residual, then process was stopped and the
optimal model was obtained. This technique proposed was applied in
both generated random and financial time series data.

Keywords

time series, MODWT, Mutual Information, mRMR

References

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