| 參考文獻: | Babaei, G., & Bamdad, S. (2020). A multi-objective instance-based decision support system for investment recommendation in peer-to-peer lending. Expert systems with Applications, 150, 113278.
Bifet, A., & Gavalda, R. (2007). Learning from time-changing data with adaptive windowing. Paper presented at the Proceedings of the 2007 SIAM international conference on data mining.
Bry, G., & Boschan, C. (1971). Front matter to" Cyclical Analysis of Time Series: Selected Procedures and Computer Programs". In Cyclical analysis of time series: Selected procedures and computer programs (pp. -13--12): NBEr.
Chang, H.-Y. (2019). The sequentially-learning-based algorithm and the prediction of the turning points of bull and bear markets, Master thesis, National Chengchi University, Taipei.
Chauvet, M., & Potter, S. (2000). Coincident and leading indicators of the stock market. Journal of Empirical Finance, 7(1), 87-111.
Chen, S.-S. (2009). Predicting the bear stock market: Macroeconomic variables as leading indicators. Journal of Banking & Finance, 33(2), 211-223.
Chen, S.-S. (2012). Revisiting the empirical linkages between stock returns and trading volume. Journal of Banking & Finance, 36(6), 1781-1788.
Chiang, W.-C., Enke, D., Wu, T., & Wang, R. (2016). An adaptive stock index trading decision support system. Expert systems with Applications, 59, 195-207.
Chiu, D.-Y., Shiu, C.-Y., & Lin, Y.-S. (2011). USA S&P 500 stock market dynamism exploration with moving window and artificial intelligence approach. Paper presented at the The 7th International Conference on Networked Computing and Advanced Information Management.
Deng, H., & Wibowo, S. (2008). A rule-based decision support system for evaluating and selecting IS projects. Paper presented at the Proceedings of the international multiconference of engineers and computer scientists.
Du, W., Leung, S. Y. S., & Kwong, C. K. (2014). Time series forecasting by neural networks: A knee point-based multiobjective evolutionary algorithm approach. Expert systems with Applications, 41(18), 8049-8061.
Giachetti, R. E. (1998). A decision support system for material and manufacturing process selection. Journal of Intelligent Manufacturing, 9(3), 265-276.
Goodwin, T. H. (1993). Business-cycle analysis with a Markov-switching model. Journal of Business & Economic Statistics, 11(3), 331-339.
Gottschlich, J., & Hinz, O. (2014). A decision support system for stock investment recommendations using collective wisdom. Decision support systems, 59, 52-62.
Gu, C.-S., Hsieh, H.-P., Wu, C.-S., Chang, R.-I., & Ho, J.-M. (2019). A Fund Selection Robo-Advisor with Deep-learning Driven Market Prediction. Paper presented at the 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC).
Hamilton, J. D. (2011). Calling recessions in real time. International Journal of Forecasting, 27(4), 1006-1026.
Hanna, A. J. (2018). A top-down approach to identifying bull and bear market states. International Review of Financial Analysis, 55, 93-110.
He, H. (2011). Self-adaptive systems for machine intelligence: John Wiley & Sons.
Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural networks, 2(5), 359-366.
Hota, H., Handa, R., & Shrivas, A. (2017). Time series data prediction using sliding window based RBF neural network. International Journal of Computational Intelligence Research, 13(5), 1145-1156.
Huang, S.-Y., Lin, J.-W., & Tsaih, R.-H. (2016). Outlier detection in the concept drifting environment. Paper presented at the 2016 International Joint Conference on Neural Networks (IJCNN).
Keen, P. G. (1978). Decisión support systems; an organizational perspective. Retrieved from
Klein, M. R., & Methlie, L. B. (1995). Knowledge-based decision support systems: with applications in business: John Wiley & Sons, Inc.
Kong, G., Xu, D.-L., Body, R., Yang, J.-B., Mackway-Jones, K., & Carley, S. (2012). A belief rule-based decision support system for clinical risk assessment of cardiac chest pain. European Journal of Operational Research, 219(3), 564-573.
Krstic, M., & Bjelica, M. (2012). Context-aware personalized program guide based on neural network. IEEE transactions on consumer electronics, 58(4), 1301-1306.
Kumar, I., Dogra, K., Utreja, C., & Yadav, P. (2018). A comparative study of supervised machine learning algorithms for stock market trend prediction. Paper presented at the 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT).
Lauren, S., & Harlili, S. D. (2014). Stock trend prediction using simple moving average supported by news classification. Paper presented at the 2014 International Conference of Advanced Informatics: Concept, Theory and Application (ICAICTA).
Lazarescu, M. M., Venkatesh, S., & Bui, H. H. (2004). Using multiple windows to track concept drift. Intelligent data analysis, 8(1), 29-59.
Lin, Y., Guo, H., & Hu, J. (2013). An SVM-based approach for stock market trend prediction. Paper presented at the The 2013 international joint conference on neural networks (IJCNN).
Liu, Y.-C., & Yeh, I. (2017). Using mixture design and neural networks to build stock selection decision support systems. Neural Computing and Applications, 28(3), 521-535.
Maheu, J. M., & McCurdy, T. H. (2000). Identifying bull and bear markets in stock returns. Journal of Business & Economic Statistics, 18(1), 100-112.
Martinez, L., Ruan, D., & Herrera, F. (2010). Computing with words in decision support systems: an overview on models and applications. International Journal of Computational Intelligence Systems, 3(4), 382-395.
Nti, K. O., Adekoya, A., & Weyori, B. (2019). Random forest based feature selection of macroeconomic variables for stock market prediction. American Journal of Applied Sciences, 16(7), 200.212.
Nyberg, H. (2013). Predicting bear and bull stock markets with dynamic binary time series models. Journal of Banking & Finance, 37(9), 3351-3363.
Padmanabhan, R., Meskin, N., Khattab, T., Shraim, M., & Al-Hitmi, M. (2021). Reinforcement learning-based decision support system for COVID-19. Biomedical Signal Processing and Control, 68, 102676.
Pagan, A. R., & Sossounov, K. A. (2003). A simple framework for analysing bull and bear markets. Journal of applied econometrics, 18(1), 23-46.
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., . . . Antiga, L. (2019). Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, 32.
Ratanapakorn, O., & Sharma, S. C. (2007). Dynamic analysis between the US stock returns and the macroeconomic variables. Applied Financial Economics, 17(5), 369-377.
Safdar, S., Zafar, S., Zafar, N., & Khan, N. F. (2018). Machine learning based decision support systems (DSS) for heart disease diagnosis: a review. Artificial Intelligence Review, 50(4), 597-623.
Sazli, M. H. (2006). A brief review of feed-forward neural networks. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, 50(01).
Schumaker, R. P., & Chen, H. (2009). Textual analysis of stock market prediction using breaking financial news: The AZFin text system. ACM Trans. Inf. Syst., 27(2), Article 12. doi:10.1145/1462198.1462204
Shen, J., & Shafiq, M. O. (2020). Short-term stock market price trend prediction using a comprehensive deep learning system. Journal of big Data, 7(1), 1-33.
Singh, R., & Balasundaram, S. (2007). Application of extreme learning machine method for time series analysis. International Journal of Intelligent Technology, 2(4), 256-262.
Tran, D. T., Kanniainen, J., Gabbouj, M., & Iosifidis, A. (2019). Data-driven neural architecture learning for financial time-series forecasting. arXiv preprint arXiv:1903.06751.
Tsaih, R.-H., Kuo, B.-S., Lin, T.-H., & Hsu, C.-C. (2018). The use of big data analytics to predict the foreign exchange rate based on public media: A machine-learning experiment. It Professional, 20(2), 34-41.
Tsymbal, A. (2004). The problem of concept drift: definitions and related work. Computer Science Department, Trinity College Dublin, 106(2), 58.
Turban, E. (1995). Decision support and expert systems Management support systems: Prentice-Hall, Inc.
Wen, Q., Yang, Z., Song, Y., & Jia, P. (2010). Automatic stock decision support system based on box theory and SVM algorithm. Expert systems with Applications, 37(2), 1015-1022.
Yahyaoui, A., Jamil, A., Rasheed, J., & Yesiltepe, M. (2019). A decision support system for diabetes prediction using machine learning and deep learning techniques. Paper presented at the 2019 1st International Informatics and Software Engineering Conference (UBMYK).
Žliobaitė, I. (2010). Learning under concept drift: an overview. arXiv preprint arXiv:1010.4784. |
