Please use this identifier to cite or link to this item:
https://ah.lib.nccu.edu.tw/handle/140.119/124709| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 蔡瑞煌<br>盧敬植 | zh_TW |
| dc.contributor.advisor | Tsaih, Rua-Huan<br>Lu, Chinh-Chih | en_US |
| dc.contributor.author | 張瑄芸 | zh_TW |
| dc.contributor.author | Chang, Hsuan-Yun | en_US |
| dc.creator | 張瑄芸 | zh_TW |
| dc.creator | Chang, Hsuan-Yun | en_US |
| dc.date | 2019 | en_US |
| dc.date.accessioned | 2019-08-07T08:06:39Z | - |
| dc.date.available | 2019-08-07T08:06:39Z | - |
| dc.date.issued | 2019-08-07T08:06:39Z | - |
| dc.identifier | G0106356017 | en_US |
| dc.identifier.uri | http://nccur.lib.nccu.edu.tw/handle/140.119/124709 | - |
| dc.description | 碩士 | zh_TW |
| dc.description | 國立政治大學 | zh_TW |
| dc.description | 資訊管理學系 | zh_TW |
| dc.description | 106356017 | zh_TW |
| dc.description.abstract | 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。\n為了解決上述挑戰，本研究提出了DSM（決策支持機制）和CSI（強記、軟化、整合）學習演算法。決策支持機制運用了移動視窗的概念，不僅可以識別牛市/熊市中的潛在轉折點檢測，還可以幫助決策者檢視所有轉折點候選者。所提出的CSI學習算法具有以下特點：\n（1）採用單層隱藏層的神經網絡（ASLFN）和ReLU激活函數；\n（2）採用LTS原理加速訓練時間；\n（3）完美的學習所有訓練的數據；\n（4）實行正則化（regularization），軟化和整合機制，以減輕過度擬合趨勢的模型。\n我們進行了檢測牛市/熊市轉折點的實驗，以驗證所提出的演算法具有有效性和效率，以及偵測出轉折點候選人幫助決策者作出最終決定。 | zh_TW |
| dc.description.abstract | 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.\nTo 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. | en_US |
| dc.description.tableofcontents | 1. Introduction 7\n2. Literature Review 9\n2.1 The prediction of bull and bear markets 9\n2.2 Concept drifting 10\n2.3 The single-hidden layer feed-forward neural networks with single output node …………………………………………………………………………………11\n2.4 The back-propagation learning algorithm associated with SLFN with single output node 12\n2.5 The adaptive single-hidden layer feed-forward neural networks with single output node 13\n2.6 Least Trimmed Squares estimator 14\n2.7 Moving Windows 14\n2.8 TensorFlow & GPU 15\n3. The proposed DSM and CSI learning algorithm 17\n3.1. The proposed DSM 17\n3.2 The proposed CSI learning algorithm 18\n4. Experiment design 25\n4.1. The bull and bear market in US stock market 25\n4.2. Variable description 25\n4.3. The description of collected data 27\n4.4. The proposed learning algorithm and its implementation 29\n5. Experiment results 30\n5.1. The performance evaluation 30\n5.2. The experiment results 30\n6. Conclusion and future work 36\nReference 37 | zh_TW |
| dc.format.extent | 1090335 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.source.uri | http://thesis.lib.nccu.edu.tw/record/#G0106356017 | en_US |
| dc.subject | 概念飄移 | zh_TW |
| dc.subject | 離群值偵測 | zh_TW |
| dc.subject | 人工神經網路 | zh_TW |
| dc.subject | 決策支援機制 | zh_TW |
| dc.subject | 移動視窗 | zh_TW |
| dc.subject | concept drifting | en_US |
| dc.subject | outlier detection | en_US |
| dc.subject | artificial neural network | en_US |
| dc.subject | moving window | en_US |
| dc.subject | cramming mechanism | en_US |
| dc.subject | softening mechanism | en_US |
| dc.subject | integrating mechanism | en_US |
| dc.title | 以序列型學習演算法預測牛市與熊市之轉折點 | zh_TW |
| dc.title | The sequentially-learning-based algorithm and the pre-diction of the turning points of bull and bear markets | en_US |
| dc.type | thesis | en_US |
| dc.relation.reference | [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.\n[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.\n[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.\n[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.\n[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.\n[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.\n[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\n[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.\n[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.\n[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.\n[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.\n[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.\n[13] B. Krawczyk, & M. Woźniak, “Diversity measures for one-class classifier ensembles,” Neurocomputing, vol. 126, pp. 36-44, 2014.\n[14] R. R. Tsaih, “The softening learning procedure,” Mathematical and computer model-ling, vol. 18, no. 8, pp. 61-64, 1993.\n[15] R. R. Tsaih, “An explanation of reasoning neural networks,” Mathematical and Com-puter Modelling, vol. 28, no. 2, pp. 37-44, 1998.\n[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.\n[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.\n[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.\n[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.\n[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.\n[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.\n[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.\n[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.\n[24] C. W. Lin, A Decision Support Mechanism for Outlier Detection in the Concept Drift-ing Environment, Master`s thesis, 2015.\n[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.\n[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.\n[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.\n[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.\n[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.\n[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. | zh_TW |
| dc.identifier.doi | 10.6814/NCCU201900325 | en_US |
| item.fulltext | With Fulltext | - |
| item.grantfulltext | open | - |
| item.cerifentitytype | Publications | - |
| item.openairetype | thesis | - |
| item.openairecristype | http://purl.org/coar/resource_type/c_46ec | - |
| Appears in Collections: | 學位論文 | |
Files in This Item:
| File | Size | Format | |
|---|---|---|---|
| 601701.pdf | 1.06 MB | Adobe PDF2 | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.