Multistep Forecasting Non-Stationary Time Series using Wavelets and Kernel Smoothing

Mina Aminghafari 1 Jean-Michel Poggi 2, 3
3 SELECT - Model selection in statistical learning
Inria Saclay - Ile de France, LMO - Laboratoire de Mathématiques d'Orsay, CNRS - Centre National de la Recherche Scientifique : UMR
Abstract : The authors deal with forecasting nonstationary time series using wavelets and kernel smoothing. Starting from a basic forecasting procedure based on the regression of the process on the nondecimated Haar wavelet coefficients of the past, the procedure was extended in various directions, including the use of an arbitrary wavelet or polynomial fitting for extrapolating low-frequency components. The authors study a further generalization of the prediction procedure dealing with multistep forecasting and combining kernel smoothing and wavelets. They finally illustrate the proposed procedure on nonstationary simulated and real data and then compare it to well-known competitors.
Type de document :
Article dans une revue
Communications in Statistics - Theory and Methods, Taylor & Francis, 2012, 41 (3), pp.485-499
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https://hal.inria.fr/hal-00778125
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Soumis le : vendredi 18 janvier 2013 - 17:35:23
Dernière modification le : mardi 27 mars 2018 - 18:02:33

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  • HAL Id : hal-00778125, version 1

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Mina Aminghafari, Jean-Michel Poggi. Multistep Forecasting Non-Stationary Time Series using Wavelets and Kernel Smoothing. Communications in Statistics - Theory and Methods, Taylor & Francis, 2012, 41 (3), pp.485-499. 〈hal-00778125〉

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