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A wavelet lifting approach to long-memory estimation

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JournalStatistics and computing
DateAccepted/In press - 12 Aug 2016
DateE-pub ahead of print - 3 Sep 2016
DatePublished (current) - Nov 2017
Issue number6
Volume27
Number of pages19
Pages (from-to)1453–1471
Early online date3/09/16
Original languageEnglish

Abstract

Reliable estimation of long-range dependence parameters is vital in time series. For example, in environmental and climate science such estimation is often key to understanding climate dynamics, variability and often prediction. The challenge of data collection in such disciplines means that, in practice, the sampling pattern is either irregular or blighted by missing observations. Unfortunately, virtually all existing Hurst parameter estimation methods assume regularly sampled time series and require modification to cope with irregularity or missing data. However, such interventions come at the price of inducing higher estimator bias and variation, often worryingly ignored. This article proposes a new Hurst exponent estimation method which naturally copes with data sampling irregularity. The new method is based on a multiscale lifting transform exploiting its ability to produce wavelet-like coefficients on irregular data and, simultaneously, to effect a necessary powerful decorrelation of those coefficients. Simulations show that our method is accurate and effective, performing well against competitors even in regular data settings. Armed with this evidence our method sheds new light on long-memory intensity results in environmental and climate science applications, sometimes suggesting that different scientific conclusions may need to be drawn.

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© 2016, The Authors.

    Research areas

  • Hurst exponent, Irregular sampling, Long-range dependence, Wavelets

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