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Activity Number: 15 - Networks, Multivariate Analysis, and Time Series
Type: Topic Contributed
Date/Time: Sunday, July 30, 2017 : 2:00 PM to 3:50 PM
Sponsor: Royal Statistical Society
Abstract #322749 View Presentation
Title: A Wavelet Lifting Approach to Long Memory Estimation
Author(s): Matthew Nunes* and Marina Knight and Guy Nason
Companies: Lancaster University and University of York and University of Bristol
Keywords: time series ; long memory ; Hurst exponent ; irregular observations ; missingness ; wavelet lifting

Reliable estimation of long-range dependence (LRD) parameters, such as the Hurst exponent, is a well-studied problem in the statistical literature. However, many time series observed in practice present missingness or are naturally irregularly-sampled. In these settings, current literature is sparse; most approaches require heavy modifications to deal with the irregular observations.

In this talk we present a technique for estimating the Hurst exponent of a long memory time series. The method is based on a flexible wavelet transform built via the lifting scheme, and is naturally suitable for series exhibiting time domain irregularity. The technique provides good estimation for regularly- as well as irregularly-spaced series.

We illustrate the technique through a time series application in climatology.

Authors who are presenting talks have a * after their name.

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