Cookies?
Library Header Image
LSE Research Online LSE Library Services

Forecasting non-stationary time series by wavelet process modelling

Fryzlewicz, Piotr and van Bellegem, Sébastien and von Sachs, Rainer (2003) Forecasting non-stationary time series by wavelet process modelling. Annals of the Institute of Statistical Mathematics, 55 (4). pp. 737-764. ISSN 0020-3157

[img]
Preview
PDF - Accepted Version
Download (429kB) | Preview

Identification Number: 10.1007/BF02523391

Abstract

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.

Item Type: Article
Official URL: http://www.springer.com/statistics/journal/10463
Additional Information: © 2003 The Institute of Statistical Mathematics
Subjects: H Social Sciences > HA Statistics
Sets: Departments > Statistics
Date Deposited: 20 Nov 2009 14:45
Last Modified: 25 Nov 2011 11:16
URI: http://eprints.lse.ac.uk/id/eprint/25830

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics