Abstract
The relevance vector machine (RVM) is a Bayesian version of the support vector machine, which with a sparse model representation has appeared to be a powerful tool for time-series forecasting. The RVM has demonstrated better performance over other methods such as neural networks or autoregressive integrated moving average based models. This study proposes a hybrid model that combines wavelet-based feature extractions with RVM models to forecast stock indices. The time series of explanatory variables are decomposed using some wavelet bases and the extracted time-scale features serve as inputs of an RVM to perform the non-parametric regression and forecasting. Compared with traditional forecasting models, our proposed method performs best. The root-mean-squared forecasting errors are significantly reduced.
| Original language | English |
|---|---|
| Pages (from-to) | 133-149 |
| Number of pages | 17 |
| Journal | Expert Systems |
| Volume | 25 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 2008 May 1 |
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All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Theoretical Computer Science
- Computational Theory and Mathematics
- Artificial Intelligence
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Combining wavelet-based feature extractions with relevance vector machines for stock index forecasting. / Huang, Shian-Chang; Wu, Tung-Kuang.
In: Expert Systems, Vol. 25, No. 2, 01.05.2008, p. 133-149.Research output: Contribution to journal › Article
TY - JOUR
T1 - Combining wavelet-based feature extractions with relevance vector machines for stock index forecasting
AU - Huang, Shian-Chang
AU - Wu, Tung-Kuang
PY - 2008/5/1
Y1 - 2008/5/1
N2 - The relevance vector machine (RVM) is a Bayesian version of the support vector machine, which with a sparse model representation has appeared to be a powerful tool for time-series forecasting. The RVM has demonstrated better performance over other methods such as neural networks or autoregressive integrated moving average based models. This study proposes a hybrid model that combines wavelet-based feature extractions with RVM models to forecast stock indices. The time series of explanatory variables are decomposed using some wavelet bases and the extracted time-scale features serve as inputs of an RVM to perform the non-parametric regression and forecasting. Compared with traditional forecasting models, our proposed method performs best. The root-mean-squared forecasting errors are significantly reduced.
AB - The relevance vector machine (RVM) is a Bayesian version of the support vector machine, which with a sparse model representation has appeared to be a powerful tool for time-series forecasting. The RVM has demonstrated better performance over other methods such as neural networks or autoregressive integrated moving average based models. This study proposes a hybrid model that combines wavelet-based feature extractions with RVM models to forecast stock indices. The time series of explanatory variables are decomposed using some wavelet bases and the extracted time-scale features serve as inputs of an RVM to perform the non-parametric regression and forecasting. Compared with traditional forecasting models, our proposed method performs best. The root-mean-squared forecasting errors are significantly reduced.
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UR - http://www.scopus.com/inward/citedby.url?scp=42949171426&partnerID=8YFLogxK
U2 - 10.1111/j.1468-0394.2008.00443.x
DO - 10.1111/j.1468-0394.2008.00443.x
M3 - Article
AN - SCOPUS:42949171426
VL - 25
SP - 133
EP - 149
JO - Expert Systems
JF - Expert Systems
SN - 0266-4720
IS - 2
ER -