中文部分
1.朱慧祺(2008)。資料探勘乳部腫瘤存活分析模式之建構。天主教輔仁大學管理學研究所未出版碩士論文,台北縣。2.李天行、唐筱菁(2004)。「整合財務比率與智慧資本於企業危機診斷模式之建構-類神經網路與多元適應性雲形迴歸之應用」。資訊管理學報,11(2),161-189。3.李良俊(2003)。台灣股票市場技術分析有效性之研究。實踐大學企業管理研究所,台北市。
4.李美樺(2002)。台灣綜合證券商信用評等實證模型之研究。國立中正大學企業管理研究所,嘉義縣。
5.周湘蘭(2002)。類神經網路在多重產品需求預測上之應用。元智大學,工業工程與管理學系,桃園縣。
6.吳聲昌(2006)。以資料探勘技術於台灣股票市場尋找低風險投資組合之研究。世新大學,資訊管理學系,台北市。
7.林其鴻(2005)。資料探勘於財務時間序列預測模式之建構-以日經225期貨與現貨指數為例。天主教輔仁大學金融研究所未出版碩士論文,台北縣。8.林瑞山(2003)。類神經網路於預測晶圓測試良率之應用。國立成功大學工學院工程管理專班,台南市。
9.張炯堯、張奕瑞(2005)。「以互相關檢測理論開發無響室吸音係數測試法之研究-以多孔性材料為樣本」。技術學刊,20,241-248。
10.許峻源(2001)。類神經網路與MARS於資料探勘分類模式之應用。天主教輔仁大學應用統計研究所未出版碩士論文,台北縣。11.陳姿先(2003)。美國國庫券與歐洲美元利率期貨價格間預測關係之探討-根據時間序列與人工智慧模型。國立成功大學財務金融研究所,台南市。
12.陳學群(2006)。應用獨立成份分析、支援向量迴歸及類神經網路於財務時間序列預測模式之建構。天主教輔仁大學應用統計研究所未出版碩士論文,台北縣。13.黃明輝(2002)。資料探勘在財務領域的應用-以債券型基金之績效評估為例。天主教輔仁大學金融研究所未出版碩士論文,台北縣。14.劉彥均(2008)。結合獨立成分分析與支援向量迴歸之財務時間序列預測模式。天主教輔仁大學管理學研究所未出版碩士論文,台北縣。英文部分
1.Abraham, A., Steinberg, D., & Philip, N. S. (2001). Rainfall forecasting using soft computing models and multivariate adaptive regression splines. IEEE SMC transactions: Special, 2001, 1-12.
2.Anderson, J. A., & Rosenfeld, E. (1998). Neurocomputing: Foundation of Research. Cambridge, MA: MIT Press.
3.Balachandher, K. G., Fauzias, M. N., & Lai, M. M. (2002). An examination of the random walk model and technique trading rules in the Malaysian stock market. Quarterly Journal of Business & Economics, 41, 81-104.
4.Bodyanskiy, Y., & Popov, S. (2006). Neural network approach to forecasting of quasiperiodic financial time series. European Journal of Operational Research, 175, 1357-1366.
5.Cao, Q., Leggio, K. B., & Schniederjans, M. J. (2005). A comparison between Fama and French's model and artificial neural networks in predicting the Chinese stock market. Computers & Operations Research, 32, 2499-2512.
6.Carven, M. W., & Shavlik, J. W. (1997). Using neural networks for data mining. Future Generation Computer System, 13, 221-229.
7.Chen, A. S., Leung, M. T., & Daouk, H. (2003). Application of neural network to an emerging financial market: Forecasting and trading the Taiwan stock index. Computers Operations Research, 30, 901-923.
8.Chen, S. H., & Yeh, C. H. (2002). On the emergent properties of artificial stock markets: the efficient market hypothesis and the rational expectations hypothesis. Journal of Economic Behavior & Organization, 49, 217-239.
9.Chou, S. M., Lee, T. S., Shao, Y. E., & Chen, I. F. (2004). Mining the breast cancer pattern using artificial neural networks and multivariate adaptive regression splines. Expert Systems with Applications, 27(1), 133-142.
10.Chung, H. M., & Gray, P. (1999). Special section: Data mining. Journal of Management Information Systems, 16, 11-16.
11.Craig, K. J., Stander, N., Dooge, D. A., & Varadappa, S. (2005). Automotive crashworthiness design using response surface-based variable screening and optimization. Engineering Computations, 22, 38-61.
12.De Veaux, R. D., Gordon, A. L., Comiso, J. C., & Bacherer, N. E. (1993). Modeling of topographic effects on Antarctic sea ice using multivariate adaptive regression splines. Journal of Geophysical Research, 98(C11), 20307-20319.
13.De Veaux, R. D., Psichogios, D. C., & Ungar, L. H. (1993). A comparison of two nonparametric estimation schemes: MARS and neural networks. Computers & Chemical Engineering, 17, 819-837.
14.Fama, E. F. (1970). Efficient capital market: A review of theory and empirical. Journal of Finance, 25, 383-417.
15.Friedman, J. H. (1990). Multivariate adaptive regression splines. Stanford University, Technical Report, 102 Rev.
16.Friedman, J. H. (1991). Multivariate adaptive regression splines (with discussion). The Annals of Statistics, 19, 1-141.
17.Ghiassi, M., Saidaneb, H., & Zimbra, D. K. (2005). A dynamic artificial neural network model for forecasting time series events. International Journal of Forecasting, 21, 341-362.
18.Gitzendanner, M. A., & Soltis, P. S. (2000). Patterns of genetic variation in rare and widespread plant congeners. American Journal of Botany, 87, 783-792.
19.Greg, T. (2001). Neural network forecasting of Canadian GDP growth. International Journal of Forecasting, 17, 57-69.
20.Griffin, W. L., Fisher, N. I., Friedman, J. H., & Ryan, C. G. (1997). Statistical techniques for the classification of Chromites in diamond exploration samples. Journal of Geochemical Exploration, 59, 233-249.
21.Hwarng, H. B. (2001). Insights into neural-network forecasting of time series corresponding to ARMA (p,q) structures. Omega, 29, 273-289.
22.Ilan, A., Min, Q., Sadowski, R. J. (2001). Forecasting aggregate retail sales: a comparison of artificial neural networks and traditional methods. Journal of Retailing and Consumer Services, 8, 147-156.
23.Kanas, A., & Yannopoulos, A. (2001). Comparing linear and nonlinear forecasts for stock returns. International Review of Economics and Finance, 10, 383-398.
24.Khemchandani, R., Jayadeva, & Chandra, S. (2009). Regularized least squares fuzzy support vector regression for financial time series forecasting. Expert Systems with Applications, 36, 132-138.
25.Kiran, N. J., & Ravi, V. (2008). Software reliability prediction by soft computing techniques. The Journal of Systems and Software, 81, 576-583.
26.Ko, M., & Osei-Bryson, K.M. (2004). Using regression splines to assess the impact of information technology investments on productivity in the health care industry. Information Systems Journal, 14, 43-63(21).
27.Larry, M., Efraim, T., & Robert, R. T. (1993). Neural network fundamentals for financial analysts. In Robert R. Trippi, & Efraim Turban. Neural Networks: In Finance and Investing, Chicago, Probus Publishing Company, 1993, 3-25.
28.Law, R. (2000). Back-propagation learning in improving the accuracy of neural network-based tourism demand forecasting. Tourism Management, 21, 331-340.
29.Lee, T. S., & Chen, I. F. (2005). A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines. Expert Systems with Applications, 28, 743-752.
30.Lee, T. S., & Chiu, C. C. (2002). Neural network forecasting of an opening cash price index. International Journal of Systems Science, 33, 229-237.
31.Lee, T. S., Chiu, C. C., Chou, Y. C., & Lu, C. J. (2006). Mining the customer credit using classification and regression tree and multivariate adaptive regression splines. Computational Statistics & Data Analysis, 50, 1113-1130.
32.Leigh, W., Hightower, R., & Modani, N. (2005). Forecasting the New York stock exchange composite index with past price and interest rate on condition of volume spike. Expert Systems with Applications, 28, 1-8.
33.Malkiel, B. G. (2005). Reflections on the efficient market hypothesis: 30 years later. The Financial Review, 40, 1-9.
34.Moisen, G. G., & Frescino, T. S. (2002). Comparing five modelling techniques for predicting forest characteristics. Ecological Modelling, 157, 209-225.
35.Nguyen-Cong, V., Van, D. G., & Rode, B. M. (1996). Using multivariate adaptive regression splines to QSAR studies of dihydroartemisinin derivatives. European Journal of Medicinal Chemistry, 31, 797-803.
36.Roh, T. H. (2007). Forecasting the volatility of stock price index. Expert Systems with Applications, 33, 916-922.
37.Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagation errors. Nature, 323, 533-536.
38.Silva, A. P. D. (2001). Efficient variable screening for multivariate analysis. Journal of Multivariate Analysis, 76, 35-62.
39.Steinberg, D., Bernard, B., Phillip, C., & Kerry, M. (1999). MARS user guide. San Diego, CA: Salford Systems Inc.
40.Tang, Z., & Fishwick, P. A. (1993). Feed forward neural nets as models for time series forecasting. ORSA Journal on Computing, 5, 374-385.
41.Timmermann, A., & Granger, C. W. J. (2004). Efficient market hypothesis and forecasting. International Journal of Forecasting, 20, 15-27.
42.Vellido, A., Lisboa, P. J. G., & Vaughan, J. (1999). Neural networks in business: a survey of applications. Expert Systems with Applications, 17, 51-70.
43.Wei, Z., Varela, O., D'Souza, J., & Hassan, M. K. (2003). The financial and operating performance of China's newly privatized firms. Financial Management, 107-126.
44.Zareipour, H., Bhattacharya, K., & Cañizares, C.A. (2006). Forecasting the Hourly Ontario Energy Price by Multivariate Adaptive Regression Splines. Power Engineering Society General Meeting, 1-7.
45.Zhang, G., Patuwo, B. E., & Hu, M. Y. (1998). Forecasting with artificial neural network: The state of the art. International Journal of Forecasting, 14, 35-62.
46.Zhou, Y., & Leung, H. (2007). Predicting objected-oriented software maintainability using multivariate adaptive regression splines. The Journal of Systems and Software, 80, 1349-1361.