Abstract
Due to the high risk associated with international transactions, exchange rate forecasting is a challenging and important field in modern time series analysis. The difficulty in forecasting arises from the nonlinearity and non-stationarity inherent in exchange rate dynamics. To address these problems, this study proposes a hybrid model that couples two effective feature extraction techniques, phase space reconstruction, and wavelet analysis, with a Relevance Vector Regression (RVR) model to forecast chaotic exchange rates. The time series inputs are first mapped into high-dimension phase space, and then the phase space signal is decomposed on a wavelet basis to analyze its dynamics under various frequencies. Finally, each wavelet component is fed into a local RVR to perform non-parametric regression and forecasting. Compared with other forecasting models, such as support vector machines (SVR), RVR, GJR-GARCH or pure wavelet-base models, the proposed model performs best and statistically improves forecasting performance under root mean square error (RMSE), mean absolute error (MAE) and directional symmetry (DS).
| Original language | English |
|---|---|
| Pages (from-to) | 1917-1930 |
| Number of pages | 14 |
| Journal | International Journal of Innovative Computing, Information and Control |
| Volume | 8 |
| Issue number | 3 A |
| Publication status | Published - 2012 Mar 1 |
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All Science Journal Classification (ASJC) codes
- Software
- Theoretical Computer Science
- Information Systems
- Computational Theory and Mathematics
Cite this
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Wavelet-based relevance vector regression model coupled with phase space reconstruction for exchange rate forecasting. / Huang, Shian Chang; Hsieh, Chia Hsun.
In: International Journal of Innovative Computing, Information and Control, Vol. 8, No. 3 A, 01.03.2012, p. 1917-1930.Research output: Contribution to journal › Article
TY - JOUR
T1 - Wavelet-based relevance vector regression model coupled with phase space reconstruction for exchange rate forecasting
AU - Huang, Shian Chang
AU - Hsieh, Chia Hsun
PY - 2012/3/1
Y1 - 2012/3/1
N2 - Due to the high risk associated with international transactions, exchange rate forecasting is a challenging and important field in modern time series analysis. The difficulty in forecasting arises from the nonlinearity and non-stationarity inherent in exchange rate dynamics. To address these problems, this study proposes a hybrid model that couples two effective feature extraction techniques, phase space reconstruction, and wavelet analysis, with a Relevance Vector Regression (RVR) model to forecast chaotic exchange rates. The time series inputs are first mapped into high-dimension phase space, and then the phase space signal is decomposed on a wavelet basis to analyze its dynamics under various frequencies. Finally, each wavelet component is fed into a local RVR to perform non-parametric regression and forecasting. Compared with other forecasting models, such as support vector machines (SVR), RVR, GJR-GARCH or pure wavelet-base models, the proposed model performs best and statistically improves forecasting performance under root mean square error (RMSE), mean absolute error (MAE) and directional symmetry (DS).
AB - Due to the high risk associated with international transactions, exchange rate forecasting is a challenging and important field in modern time series analysis. The difficulty in forecasting arises from the nonlinearity and non-stationarity inherent in exchange rate dynamics. To address these problems, this study proposes a hybrid model that couples two effective feature extraction techniques, phase space reconstruction, and wavelet analysis, with a Relevance Vector Regression (RVR) model to forecast chaotic exchange rates. The time series inputs are first mapped into high-dimension phase space, and then the phase space signal is decomposed on a wavelet basis to analyze its dynamics under various frequencies. Finally, each wavelet component is fed into a local RVR to perform non-parametric regression and forecasting. Compared with other forecasting models, such as support vector machines (SVR), RVR, GJR-GARCH or pure wavelet-base models, the proposed model performs best and statistically improves forecasting performance under root mean square error (RMSE), mean absolute error (MAE) and directional symmetry (DS).
UR - http://www.scopus.com/inward/record.url?scp=84863257783&partnerID=8YFLogxK
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M3 - Article
AN - SCOPUS:84863257783
VL - 8
SP - 1917
EP - 1930
JO - International Journal of Innovative Computing, Information and Control
JF - International Journal of Innovative Computing, Information and Control
SN - 1349-4198
IS - 3 A
ER -