政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/124709
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 11 |  全文笔数/总笔数 : 88987/118697 (75%)
造访人次 : 23580125      在线人数 : 272
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜寻范围 查询小技巧:
  • 您可在西文检索词汇前后加上"双引号",以获取较精准的检索结果
  • 若欲以作者姓名搜寻,建议至进阶搜寻限定作者字段,可获得较完整数据
  • 进阶搜寻
    政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 >  Item 140.119/124709

    请使用永久网址来引用或连结此文件: http://nccur.lib.nccu.edu.tw/handle/140.119/124709

    题名: 以序列型學習演算法預測牛市與熊市之轉折點
    The sequentially-learning-based algorithm and the pre-diction of the turning points of bull and bear markets
    作者: 張瑄芸
    Chang, Hsuan-Yun
    贡献者: 蔡瑞煌

    Tsaih, Rua-Huan
    Lu, Chinh-Chih

    Chang, Hsuan-Yun
    关键词: 概念飄移
    concept drifting
    outlier detection
    artificial neural network
    moving window
    cramming mechanism
    softening mechanism
    integrating mechanism
    日期: 2019
    上传时间: 2019-08-07 16:06:39 (UTC+8)
    摘要: 在機器學習領域中,對於人工神經網絡(ANN)的架構中,輸入值是實數,輸出值是二元的問題是有挑戰性的,尚未有任何神經網路學習演算法可以解決過度擬合(overfitting)問題,同時可以完美地學習所有訓練數據;另外,在概念飄移環境中要如何去處理離群值的偵測也變得重要,現今的很多資料多為動態性且具概念漂移的特性。
    In the field of machine learning, there is a challenge to the Artificial Neural Networks (ANN) application whose input values are real numbers and the output values are binary. Whether any of ANN learning algorithms can solve the overfitting problem, while it can perfectly learn all of training data. Besides, the problem of outlier detection in the concept environment is becoming an issue. The nature data now has the dynamic and unstable property in the concept drifting environment.
    To address the aforementioned challenge, this study proposes the DSM (Decision Support Mechanism) and CSI (Cramming, Softening, and Integrating) learning algorithm. DSM apply the moving window mechanism, and it can not only identify the potential turning point detection in the bull/ bear market but also assist the decision maker to double check merely all of turning point candidates. The proposed CSI learning algorithm has the follow-ing features: (1) the adoption of adaptive single-hidden layer feed-forward neural network (ASLFN) and ReLU activation function, (2) the usage of least trimmed squares (LTS) prin-ciple to speed up the training time, (3) the practice to precisely learn all training data, and (4) the implementations of the regularization term, the softening and integrating mechanism to alleviate the obtained model from the overfitting tendency. We conduct an experiment of detecting the turning points of bull/bear markets to validate the effectiveness and efficiency of the proposed algorithm in the addressing challenge.
    參考文獻: [1] R. R. Trippi, and E. Turban, Neural networks in finance and investing: Using artifi-cial intelligence to improve real world performance, McGraw-Hill, Inc., 1992.
    [2] H. J. Kim, and K. S. Shin, “A hybrid approach based on neural networks and genetic algorithms for detecting temporal patterns in stock markets,” Applied Soft Computing, vol. 7, no. 2, pp. 569-576, 2007.
    [3] M. Göçken, M. Özçalıcı, A. Boru, and A. T. Dosdoğru, “Integrating metaheuristics and artificial neural networks for improved stock price prediction,” Expert Systems with Applications, vol. 44, pp. 320-331, 2016.
    [4] A. Macchiarulo, “Predicting and beating the stock market with machine learning and technical analysis,” Journal of Internet Banking and Commerce, vol. 23, no. 1, pp. 1-22, 2018.
    [5] E. Chong, C. Han, and F. C. Park, “Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies,” Expert Sys-tems with Applications, vol. 83, pp. 187-205, 2017.
    [6] A. K. Nassirtoussi, S. Aghabozorgi, T. Y. Wah, and D. C. L. Ngo, “Text mining for market prediction: A systematic review,” Expert Systems with Applications, vol. 41, no. 16, pp. 7653-7670, 2014.
    [7] A. J. Hanna, “A top-down approach to identifying bull and bear market states.,” In-ternational Review of Financial Analysis, vol. 55, pp. 93-110, 2018
    [8] A. R. Pagan, and K. A. Sossounov, “A simple framework for analysing bull and bear markets,” Journal of Applied Econometrics, vol. 18, no. 1, pp. 23-46, 2003.
    [9] S. S. Chen, “Predicting the bear stock market: Macroeconomic variables as leading indicators,” Journal of Banking & Finance, vol. 33, no. 2, pp. 211-223, 2009.
    [10] S. S. Chen, “Revisiting the empirical linkages between stock returns and trading vol-ume,” Journal of Banking & Finance, vol. 36, no. 6, pp. 1781-1788, 2012.
    [11] A. Tsymbal, “The problem of concept drift: definitions and related work,” Computer Science Department, Trinity College Dublin, vol. 106, no. 2, pp. 58, 2004.
    [12] R. Elwell, & R. Polikar, “Incremental learning of concept drift in nonstationary envi-ronments,” IEEE Transactions on Neural Networks, vol. 22, no. 10, pp. 1517-1531, 2011.
    [13] B. Krawczyk, & M. Woźniak, “Diversity measures for one-class classifier ensembles,” Neurocomputing, vol. 126, pp. 36-44, 2014.
    [14] R. R. Tsaih, “The softening learning procedure,” Mathematical and computer model-ling, vol. 18, no. 8, pp. 61-64, 1993.
    [15] R. R. Tsaih, “An explanation of reasoning neural networks,” Mathematical and Com-puter Modelling, vol. 28, no. 2, pp. 37-44, 1998.
    [16] R. H. Tsaih, and T. C. Cheng, “A resistant learning procedure for coping with outli-ers,” Annals of Mathematics and Artificial Intelligence, vol. 57, no. 2, pp. 161-180, 2009.
    [17] S. Y. Huang, R. H. Tsaih, and F. Yu, “Topological pattern discovery and feature ex-traction for fraudulent financial reporting,” Expert Systems with Applications, vol. 41, no. 9, pp. 4360-4372, 2014.
    [18] R. H. Tsaih, B. S. Kuo, T. H. Lin, and C. C. Hsu, “The use of big data analytics to predict the foreign exchange rate based on public media: A machine-learning experi-ment,” IT Professional, vol. 20, no. 2, pp. 34-41, 2018.
    [19] P. J. Rousseeuw, & K. Van Driessen, “Computing LTS regression for large data sets,” Data mining and knowledge discovery, vol. 12, no. 1, pp. 29-45, 2006.
    [20] D. E. Rumelhart, G. E. Hinton, & J. L. McClelland, “A general framework for parallel distributed processing,” Parallel distributed processing: Explorations in the micro-structure of cognition, vol. 1, no. 45-76, pp. 26, 1986.
    [21] A. Zaman, P. J. Rousseeuw, & M. Orhan, “Econometric applications of high-breakdown robust regression techniques,” Economics Letters, vol. 71, no. 1, pp. 1-8, 2001.
    [22] A. C. Atkinson, & T. C. Cheng, “On robust linear regression with incomplete data,” Computational statistics & data analysis, vol. 33, no. 4, pp. 361-380, 2000.
    [23] J. Gama, I. Žliobaitė, A. Bifet, M. Pechenizkiy, and A. Bouchachia, “A survey on concept drift adaptation,” ACM computing surveys (CSUR), vol. 46, no. 4, pp. 44, 2014.
    [24] C. W. Lin, A Decision Support Mechanism for Outlier Detection in the Concept Drift-ing Environment, Master's thesis, 2015.
    [25] M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, ... & M. Kudlur, “Tensor-flow: A system for large-scale machine learning,” In 12th {USENIX} Symposium on Operating Systems Design and Implementation, {OSDI} 16, pp. 265-283, 2016.
    [26] M. Abadi, and TensorFlow, A. A. B. P., “Large-scale machine learning on heterogene-ous distributed systems,” In Proceedings of the 12th USENIX Symposium on Oper-ating Systems Design and Implementation, OSDI’16, Savannah, GA, USA , pp. 265-283, 2016.
    [27] L. Gonzalez, J. G. Powell, J. Shi, & A. Wilson, “Two centuries of bull and bear mar-ket cycles,” International Review of Economics & Finance, vol. 14 no. 4, pp. 469-486, 2005.
    [28] G. Bry, & C. Boschan, “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. 13-2. NBER, 1971.
    [29] B. Babcock, S. Babu, M. Datar, R. Motwani, & J. Widom, “Models and issues in data stream systems,” In Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, pp. 1-16, ACM, 2002.
    [30] M. N. Kashani, J. Aminian, S. Shahhosseini,& M. Farrokhi, “Dynamic crude oil foul-ing prediction in industrial preheaters using optimized ANN based moving window technique,” Chemical Engineering Research and Design, vol.90, no.7, pp.938-949, 2012.
    描述: 碩士
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0106356017
    数据类型: thesis
    DOI: 10.6814/NCCU201900325
    显示于类别:[資訊管理學系] 學位論文


    档案 大小格式浏览次数
    601701.pdf1064KbAdobe PDF0检视/开启


    社群 sharing

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 回馈