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

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 t...

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Bibliographic Details
Published in:Communications in statistics. Theory and methods 2012, Vol.41 (3), p.485-499
Main Authors: Aminghafari, Mina, Poggi, Jean-Michel
Format: Article
Language:English
Subjects:
Computer Science
Online Access:Get full text
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Summary: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.
ISSN:0361-0926
1532-415X