Wavelet packet transfer function modelling of nonstationary time series

被引:45
作者
Nason, GP
Sapatinas, T
机构
[1] Univ Bristol, Dept Math, Bristol BS8 1TH, Avon, England
[2] Univ Cyprus, Dept Math & Stat, Nicosia, Cyprus
基金
英国工程与自然科学研究理事会;
关键词
nonstationary transfer function; nondecimated wavelet packets; wind time series; WaveThresh;
D O I
10.1023/A:1013168221710
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
This article shows how a non-decimated wavelet packet transform (NWPT) can be used to model a response time series, Y, in terms of an explanatory time series, X. The proposed computational technique transforms the explanatory time series into a NWPT representation and then uses standard statistical modelling methods to identify which wavelet packets are useful for modelling the response time series. We exhibit S-Plus functions from the freeware WaveThresh package that implement our methodology. The proposed modelling methodology is applied to an important problem from the wind energy industry: how to model wind speed at a target location using wind speed and direction from a reference location. Our method improves on existing target site wind speed predictions produced by widely used industry standard techniques. However, of more importance, our NWPT representation produces models to which we can attach physical and scientific interpretations and in the wind example enable us to understand more about the transfer of wind energy from site to site.
引用
收藏
页码:45 / 56
页数:12
相关论文
共 28 条
[1]   Wavelet thresholding via a Bayesian approach [J].
Abramovich, F ;
Sapatinas, T ;
Silverman, BW .
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, 1998, 60 :725-749
[2]   Wavelet analysis and its statistical applications [J].
Abramovich, F ;
Bailey, TC ;
Sapatinas, T .
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES D-THE STATISTICIAN, 2000, 49 :1-29
[3]  
[Anonymous], 1997, ESSENTIAL WAVELETS S, DOI DOI 10.1007/978-1-4612-0709-2
[4]  
[Anonymous], 1989, Applied Statistics, DOI DOI 10.2307/2347679
[5]  
[Anonymous], LECT NOTES STAT, DOI DOI 10.1007/978-1-4612-2544-7_17
[6]  
Antoniadis A., 1997, J ITALIAN STAT ASS, V6, P97, DOI DOI 10.1007/BF03178905
[7]  
BECKER RA, 1988, NEWE S LANGUAGE
[8]  
Burrus C.S., 1998, introduction to Wavelets and Wavelet Transforms-A Primer
[9]   Orthonormal shift-invariant wavelet packet decomposition and representation [J].
Cohen, I ;
Raz, S ;
Malah, D .
SIGNAL PROCESSING, 1997, 57 (03) :251-270
[10]   ENTROPY-BASED ALGORITHMS FOR BEST BASIS SELECTION [J].
COIFMAN, RR ;
WICKERHAUSER, MV .
IEEE TRANSACTIONS ON INFORMATION THEORY, 1992, 38 (02) :713-718