A widely linear multichannel wiener filter for wind prediction

Dowell, Jethro; Weiss, Stephan; Infield, David; Chandna, Swati

doi:10.1109/ssp.2014.6884567

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…However, more recently, a number of authors have advocated modelling wind measurements as complexvalued, developing analysis tools which exploit both speed and directional information of wind time series; see, for example, Goh et al (2006) and Tanaka and Mandic (2007). These complex-valued modelling approaches have resulted in methodology for improved prediction for series such as those considered in this article Dowell et al 2014). To our knowledge, long memory estimation for stationary time series is exclusively performed using realvalued time series.…”

Section: Persistence In Wind Seriesmentioning

confidence: 99%

Long memory estimation for complex-valued time series

Knight

Nunes

2018

Stat Comput

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Long memory has been observed for time series across a multitude of fields, and the accurate estimation of such dependence, for example via the Hurst exponent, is crucial for the modelling and prediction of many dynamic systems of interest. Many physical processes (such as wind data) are more naturally expressed as a complex-valued time series to represent magnitude and phase information (wind speed and direction). With data collection ubiquitously unreliable, irregular sampling or missingness is also commonplace and can cause bias in a range of analysis tasks, including Hurst estimation. This article proposes a new Hurst exponent estimation technique for complex-valued persistent data sampled with potential irregularity. Our approach is justified through establishing attractive theoretical properties of a new complex-valued wavelet lifting transform, also introduced in this paper. We demonstrate the accuracy of the proposed estimation method through simulations across a range of sampling scenarios and complex-and real-valued persistent processes. For wind data, our method highlights that inclusion of the intrinsic correlations between the real and imaginary data, inherent in our complex-valued approach, can produce different persistence estimates than when using real-valued analysis. Such analysis could then support alternative modelling or policy decisions compared with conclusions based on real-valued estimation. Keywords Complex-valued time series • Hurst exponent • Irregular sampling • Long-range dependence • Wavelets 1 Introduction Complex-valued time series arise in many scientific fields of interest, for example digital communication and signal processing (Curtis 1985; Martin 2004), environmental series (Gonella 1972; Lilly and Gascard 2006; Adali et al. 2011) and physiology (Rowe 2005). Modelling and analysis of such series in the complex domain is not only natural, but also convenient. In addition, complex-valued time series models are often able to represent more realistic behaviour in observed physical processes; see, for example, Mandic and Goh (2009) and Sykulski et al. (2017). A particular modelling Electronic supplementary material The online version of this article (

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Section: Persistence In Wind Seriesmentioning

confidence: 99%

Long memory estimation for complex-valued time series

Knight

Nunes

2018

Stat Comput

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Cyclostationarity: New trends and applications

Napolitano

2016

Signal Processing

188

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Bootstrapping periodically autoregressive models

Ciolek

Potorski

2017

ESAIM: PS

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The main objective of this paper is to establish the residual and the wild bootstrap procedures for periodically autoregressive models. We use the least squares estimators of model’s parameters and generate their bootstrap equivalents. We prove that the bootstrap procedures for causal periodic autoregressive time series with finite fourth moments are weakly consistent. Finally, we confirm our theoretical considerations by simulations.

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A widely linear multichannel wiener filter for wind prediction

Cited by 3 publications

References 11 publications

Long memory estimation for complex-valued time series

Long memory estimation for complex-valued time series

Cyclostationarity: New trends and applications

Bootstrapping periodically autoregressive models

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