Wavelet filtering for prediction in time series analysis

Authors:
Tommaso Minerva
Affiliations:
Department of Social Sciences, University of Modena and Reggio Emilia, Reggio Emilia, Italy
Venue:
WAMUS'10 Proceedings of the 10th WSEAS international conference on Wavelet analysis and multirate systems
Year:
2010

Citing 4
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WAMUS'10 Proceedings of the 10th WSEAS international conference on Wavelet analysis and multirate systems

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Abstract

In this paper I describe a wavelet filtering approach to separate a time series, the signal, into its main components. With this approach I can separate stochastic from structural components. The statistical predictive analysis will be performed on the filtered signal while the stochastic term could be a-posteriori reintroduced through statistical simulation approaches (such as Markov Chain Monte Carlo). The proposed metodology has been applied to financial time series to predict both returns and risk.