C
Chih-Chou Chiu
Researcher at National Taipei University of Technology
Publications - 37
Citations - 1999
Chih-Chou Chiu is an academic researcher from National Taipei University of Technology. The author has contributed to research in topics: Statistical process control & Multivariate adaptive regression splines. The author has an hindex of 15, co-authored 31 publications receiving 1750 citations.
Papers
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Financial time series forecasting using independent component analysis and support vector regression
TL;DR: Experimental results show that the proposed model outperforms the SVR model with non-filtered forecasting variables and a random walk model.
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Mining the customer credit using classification and regression tree and multivariate adaptive regression splines
TL;DR: As the results reveal, CART and MARS outperform traditional discriminant analysis, logistic regression, neural networks, and support vector machine (SVM) approaches in terms of credit scoring accuracy and hence provide efficient alternatives in implementing credit scoring tasks.
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Credit scoring using the hybrid neural discriminant technique
TL;DR: The objective of the proposed study is to explore the performance of credit scoring by integrating the backpropagation neural networks with traditional discriminant analysis approach, and the proposed hybrid approach converges much faster than the conventional neural networks model.
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A hybrid approach by integrating wavelet-based feature extraction with MARS and SVR for stock index forecasting
TL;DR: A new stock price forecasting model which integrates wavelet transform, multivariate adaptive regression splines (MARS), and support vector regression (SVR) is proposed to not only address the problem of wavelet sub-series selection but also improve the forecast accuracy.
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Integration of nonlinear independent component analysis and support vector regression for stock price forecasting
TL;DR: A stock price forecasting model which first uses NLICA as preprocessing to extract features from forecasting variables is developed and improves the prediction accuracy of the SVR approach but also outperforms the PCA-SVR, LICA-S VR and single SVR methods.