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.
Wavelet filtering for prediction in Time Series Analysis / Minerva, Tommaso. - STAMPA. - (2010), pp. 89-95. (Intervento presentato al convegno 10th WSEAS International Conference on Wavelet Analysis and Multirate Systems, WAMUS '10, 9th WSEAS International Conference on Non-Linear Analysis, Non-Linear Systems and Chaos, NOLASC '10 tenutosi a Sousse, tun nel 2010).
Wavelet filtering for prediction in Time Series Analysis
MINERVA, Tommaso
2010
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.File | Dimensione | Formato | |
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