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Forecasting non-stationary time series by wavelet process modelling

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Many time series in the applied sciences display a time-varying second order structure. In this article, we address the problem of how to forecast these nonstationary time series by means of non-decimated wavelets. Using the class of Locally Stationary Wavelet processes, we introduce a new predictor based on wavelets and derive the prediction equations as a generalisation of the Yule-Walker equations. We propose an automatic computational procedure for choosing the parameters of the forecasting algorithm. Finally, we apply the prediction algorithm to a meteorological time series.

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en

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application/pdf

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http://eprints.lse.ac.uk/25830/1/Forecasting_non-stationary_time_series_by_wavelet_process_modelling%28lsero%29.pdf

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