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姓名	林育彬(Yu-Bin Lin)  查詢紙本館藏	畢業系所	統計研究所
論文名稱	視覺化股票市場之狀態變動 (Visualizing the Changing States of Stock Markets)
檔案	[Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2026-7-1以後開放)
摘要(中)	近年來，在股市投資的方法上已經有許多研究人員提出各種投資組合策略，考量到提高投 資組合的獲利，研究人員對於股票之間的連動性非常感興趣。為此，本篇論文透過時間相 似性度量指標之 t-隨機鄰域嵌入法 (DTW based on t-SNE)及階層分群法，將多維度的股 票市場資訊做為特徵，對資料做視覺化及分群處理，並運用熱圖呈現每檔股票在不同時間 段上的狀態變化。本研究之成果是希望透過本研究所提出之“股票狀態視覺化熱圖”，視覺 化不同股票間可能存在之相關性，並找出具有相似狀態變化之股票間的穩定或新興集群。 並透過股票的狀態變化進一步分析資金的流動變化。
摘要(英)	In recent years, numerous researchers have proposed various investment portfolio strategies in the field of stock market investing. To enhance portfolio returns, researchers have shown a keen interest in understanding the interrelationships between stocks. In this paper, we utilize the DTW (Dynamic Time Warping) based on t-SNE (t-Distributed Stochastic Neighbor Embedding) algorithm, along with hierarchical clustering, to visualize and cluster multidimensional stock market information. We extract multiple dimensions of data as features and present the state changes of each stock across different time periods using a heat map. The main objective of this research is to develop the “Stock State Visualization Heat Map”, which visualizes the potential correlations among different stocks and identifies stable or emerging clusters of stocks exhibiting similar state changes. Furthermore, by analyzing the changes in stock states, we aim to gain insights into the flow of funds within the market.
關鍵字(中)	★ 動態時間校正 ★ 熱圖 ★ 階層分群 ★ 資訊視覺化 ★ 股票技術分析 ★ t-隨機鄰域嵌入法	關鍵字(英)
論文目次	1 Introduction 1 2 Review of t-SNE 3 2.1 t-Distributed Stochastic Neighbor Embedding . . . . . . . . . . . . . . . . . 3 2.2 Temporal Matching-based t-SNE . . . . . . . . . . . . . . . . . . . . . . . . 8 3 Methods 12 3.1 Data Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 3.2 Dimension Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.3 Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 4 Application 19 4.1 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 4.2 Visualizing Dimension Reduction Results and Clustering Effects . . . . . . . 20 4.3 Stock State Visualization Heat Map Performance . . . . . . . . . . . . . . . 22 4.4 Stock State Visualization Heat Map with Foreign Net Buying and Selling Activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 5 Conclusion and discussion 33 Reference 35
參考文獻	D. J. Berndt and J. Clifford. Using dynamic time warping to find patterns in time series. Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining, pages 359–370, 1994. G. Hinton and S. T. Roweis. Stochastic Neighbor Embedding. Advances in Neural Information Processing Systems, volume 15, pages 833–840, Cambridge, MA, USA, 2002. The MIT Press. H. Esmalifalak, A. I. Ajirlou, S. P. Behrouz, and M. Esmalifalak. (Dis)integration levels across global stock markets: A multidimensional scaling and cluster analysis. Expert Systems with Applications, 2015. H. Sakoe and S. Chiba. Dynamic Programming Algorithm Optimization for Spoken Word Recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. 26, pages 43-49, 1978. J. A. F. Costa and M. L. D. A. Netto. Estimating the number of clusters in multivariate data by self-organizing maps. International Journal of Neural Systems, Vol. 09, No. 03, pages 195-202, 1999. J. H. Ward Jr. Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, Vol. 58, pages 236–244, 1963. J. T. Machado, F. B. Duarte, and G. M. Duarte. Analysis of stock market indices through multidimensional scaling. Commun Nonlinear Sci Numer Simulat, Vol. 16, pages 4610–4618, 2011. K. Y. Wong and F. L. Chung. Visualizing Time Series Data with Temporal Matching Based t-SNE. International Joint Conference on Neural Networks, Budapest, Hungary, pages 14-19, 2019. L. R. Williams. How I Made One Million Dollars ... Last Year ... Trading Commodities, ISBN 978-0930233105, 1979. L. v. d. Maaten and G. Hinton. Visualizing Data using t-SNE. Journal of Machine Learning Research, 2008. M. A. A. Cox. Analysis of stock market indices through multidimensional scaling. Journal of Statistical Computation and Simulation, Vol. 83, No. 11, pages 2015–2029, 2013. P. J. Rousseeuw. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, Vol. 20, pages 53-65, 1987.
指導教授	王紹宣 黃士峰	審核日期	2023-7-26
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