A wavelet denoising support vector regression ensemble model for exchange rate prediction

Kaijian He; Chi Xie; Kin Keung Lai

doi:10.1109/WiCom.2008.2342

A wavelet denoising support vector regression ensemble model for exchange rate prediction

Kaijian He
, Chi Xie
, Kin Keung Lai

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

3 Citations (Scopus)

Abstract

Based on the nonlinear ensemble and level dependent denoising framework, a novel wavelet denoising Support Vector Regression (SVR) ensemble forecasting model is proposed. The proposed model attempts to incorporate the level dependent denoising technique that utilizes the multi scale heterogeneous characteristics of data and noises into the modeling process. Forecasting results based on different wavelet parameters are firstly preprocessed by Principle Component Analysis to reduce dimensionality and noise, then ensembled via SVR to further reduce forecasting biases and improve the forecasting stability. Experiment results reveal that the performance of the proposed approach is statistically superior to those more traditional methods presented in this study in terms of the same measurement. © 2008 IEEE.

Original language	English
Title of host publication	2008 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2008
DOIs	https://doi.org/10.1109/WiCom.2008.2342
Publication status	Published - 2008
Event	2008 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2008 - Dalian, China Duration: 12 Oct 2008 → 14 Oct 2008

Conference

Conference	2008 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2008
Place	China
City	Dalian
Period	12/10/08 → 14/10/08

Research Keywords

Nonlinear ensemble
Principle component analysis
Shrinkage strategy
Support vector regression
Threshold selection strategy
Wavelet denoising model

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10.1109/WiCom.2008.2342

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@inproceedings{f56d6af87c1b47e8af02d5c5d4f13a49,

title = "A wavelet denoising support vector regression ensemble model for exchange rate prediction",

abstract = "Based on the nonlinear ensemble and level dependent denoising framework, a novel wavelet denoising Support Vector Regression (SVR) ensemble forecasting model is proposed. The proposed model attempts to incorporate the level dependent denoising technique that utilizes the multi scale heterogeneous characteristics of data and noises into the modeling process. Forecasting results based on different wavelet parameters are firstly preprocessed by Principle Component Analysis to reduce dimensionality and noise, then ensembled via SVR to further reduce forecasting biases and improve the forecasting stability. Experiment results reveal that the performance of the proposed approach is statistically superior to those more traditional methods presented in this study in terms of the same measurement. {\textcopyright} 2008 IEEE.",

keywords = "Nonlinear ensemble, Principle component analysis, Shrinkage strategy, Support vector regression, Threshold selection strategy, Wavelet denoising model",

author = "Kaijian He and Chi Xie and Lai, \{Kin Keung\}",

year = "2008",

doi = "10.1109/WiCom.2008.2342",

language = "English",

isbn = "9781424421084",

booktitle = "2008 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2008",

note = "2008 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2008 ; Conference date: 12-10-2008 Through 14-10-2008",

}

He, K, Xie, C & Lai, KK 2008, A wavelet denoising support vector regression ensemble model for exchange rate prediction. in 2008 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2008., 4680531, 2008 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2008, Dalian, China, 12/10/08. https://doi.org/10.1109/WiCom.2008.2342

A wavelet denoising support vector regression ensemble model for exchange rate prediction. / He, Kaijian; Xie, Chi; Lai, Kin Keung.
2008 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2008. 2008. 4680531.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - A wavelet denoising support vector regression ensemble model for exchange rate prediction

AU - He, Kaijian

AU - Xie, Chi

AU - Lai, Kin Keung

PY - 2008

Y1 - 2008

N2 - Based on the nonlinear ensemble and level dependent denoising framework, a novel wavelet denoising Support Vector Regression (SVR) ensemble forecasting model is proposed. The proposed model attempts to incorporate the level dependent denoising technique that utilizes the multi scale heterogeneous characteristics of data and noises into the modeling process. Forecasting results based on different wavelet parameters are firstly preprocessed by Principle Component Analysis to reduce dimensionality and noise, then ensembled via SVR to further reduce forecasting biases and improve the forecasting stability. Experiment results reveal that the performance of the proposed approach is statistically superior to those more traditional methods presented in this study in terms of the same measurement. © 2008 IEEE.

AB - Based on the nonlinear ensemble and level dependent denoising framework, a novel wavelet denoising Support Vector Regression (SVR) ensemble forecasting model is proposed. The proposed model attempts to incorporate the level dependent denoising technique that utilizes the multi scale heterogeneous characteristics of data and noises into the modeling process. Forecasting results based on different wavelet parameters are firstly preprocessed by Principle Component Analysis to reduce dimensionality and noise, then ensembled via SVR to further reduce forecasting biases and improve the forecasting stability. Experiment results reveal that the performance of the proposed approach is statistically superior to those more traditional methods presented in this study in terms of the same measurement. © 2008 IEEE.

KW - Nonlinear ensemble

KW - Principle component analysis

KW - Shrinkage strategy

KW - Support vector regression

KW - Threshold selection strategy

KW - Wavelet denoising model

UR - https://www.scopus.com/pages/publications/58049109860

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-58049109860&origin=recordpage

U2 - 10.1109/WiCom.2008.2342

DO - 10.1109/WiCom.2008.2342

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781424421084

BT - 2008 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2008

T2 - 2008 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2008

Y2 - 12 October 2008 through 14 October 2008

ER -

A wavelet denoising support vector regression ensemble model for exchange rate prediction

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