{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T16:05:19Z","timestamp":1774454719876,"version":"3.50.1"},"reference-count":26,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,11,14]],"date-time":"2022-11-14T00:00:00Z","timestamp":1668384000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Zhejiang Provincial Department of Education","award":["Y202249481"],"award-info":[{"award-number":["Y202249481"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>It is meaningful to analyze the market correlations for stock selection in the field of financial investment. Since it is difficult for existing deep clustering methods to mine the complex and nonlinear features contained in financial time series, in order to deeply mine the features of financial time series and achieve clustering, a new end-to-end deep clustering method for financial time series is proposed. It contains two modules: an autoencoder feature extraction network based on TCN (temporal convolutional neural) networks and a temporal clustering optimization algorithm with a KL (Kullback\u2013Leibler) divergence. The features of financial time series are represented by the causal convolution and the dilated convolution of TCN networks. Then, the pre-training results based on the KL divergence are fine-tuned to make the clustering results discriminative. The experimental results show that the proposed method outperforms existing deep clustering and general clustering algorithms in the CSI 300 and S&amp;P 500 index markets. In addition, the clustering results combined with an inference strategy can be used to select stocks that perform well or poorly, thus guiding actual stock market trades.<\/jats:p>","DOI":"10.3390\/fi14110331","type":"journal-article","created":{"date-parts":[[2022,11,15]],"date-time":"2022-11-15T02:31:15Z","timestamp":1668479475000},"page":"331","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Financial Market Correlation Analysis and Stock Selection Application Based on TCN-Deep Clustering"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7752-2849","authenticated-orcid":false,"given":"Yuefeng","family":"Cen","sequence":"first","affiliation":[{"name":"School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingxing","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gang","family":"Cen","sequence":"additional","affiliation":[{"name":"School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6127-4222","authenticated-orcid":false,"given":"Cheng","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Economics, Zhejiang University of Technology, Hangzhou 310014, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhigang","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1609","DOI":"10.1007\/s00521-019-04212-x","article-title":"Stock price prediction based on deep neural networks","volume":"32","author":"Yu","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"113704","DOI":"10.1016\/j.eswa.2020.113704","article-title":"Stock market forecasting with super-high dimensional time-series data using ConvLSTM, trend sampling, and specialized data augmentation","volume":"161","author":"Lee","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"67718","DOI":"10.1109\/ACCESS.2019.2916828","article-title":"Insights into LSTM fully convolutional networks for time series classification","volume":"7","author":"Karim","year":"2019","journal-title":"IEEE Access"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1145\/3309547","article-title":"Temporal relational ranking for stock prediction","volume":"37","author":"Feng","year":"2019","journal-title":"ACM Trans. Inf. Syst. (TOIS)"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Sawhney, R., Agarwal, S., Wadhwa, A., Derr, T., and Shah, R.R. (2021, January 2\u20139). Stock selection via spatiotemporal hypergraph attention network: A learning to rank approach. Proceedings of the AAAI Conference on Artificial Intelligence, Virtually.","DOI":"10.1609\/aaai.v35i1.16127"},{"key":"ref_6","first-page":"109","article-title":"Application of data mining techniques in stock markets: A survey","volume":"2","author":"Hajizadeh","year":"2010","journal-title":"J. Econ. Int. Financ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"614","DOI":"10.1016\/j.iref.2020.07.002","article-title":"Are there any other safe haven assets? Evidence for \u201cexotic\u201d and alternative assets","volume":"69","author":"Dimitriou","year":"2020","journal-title":"Int. Rev. Econ. Financ."},{"key":"ref_8","unstructured":"Kaufman, L., and Rousseeuw, P.J. (2009). Finding Groups in Data: An Introduction to Cluster Analysis, John Wiley Sons."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"115217","DOI":"10.1016\/j.eswa.2021.115217","article-title":"A similarity measurement for time series and its application to the stock market","volume":"182","author":"Zhao","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Rakthanmanon, T., and Keogh, E. (2013, January 2\u20134). Fast shapelets: A scalable algorithm for discovering time series shapelets. Proceedings of the 2013 SIAM International Conference on Data Mining, Austin, TX, USA.","DOI":"10.1137\/1.9781611972832.74"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1007\/s10115-004-0154-9","article-title":"Exact indexing of dynamic time warping","volume":"7","author":"Keogh","year":"2005","journal-title":"Knowl. Inf. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.jss.2017.09.031","article-title":"Test case prioritization for object-oriented software: An adaptive random sequence approach based on clustering","volume":"135","author":"Chen","year":"2018","journal-title":"J. Syst. Softw."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"113949","DOI":"10.1016\/j.eswa.2020.113949","article-title":"Extreme point bias compensation: A similarity method of functional clustering and its application to the stock market","volume":"164","author":"Sun","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Tian, K., Zhou, S., and Guan, J. (2017, January 18\u201322). Deepcluster: A general clustering framework based on deep learning. Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Skopje, Macedonia.","DOI":"10.1007\/978-3-319-71246-8_49"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1007\/s10618-021-00796-y","article-title":"End-to-end deep representation learning for time series clustering: A comparative study","volume":"36","author":"Lafabregue","year":"2022","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"39501","DOI":"10.1109\/ACCESS.2018.2855437","article-title":"A survey of clustering with deep learning: From the perspective of network architecture","volume":"6","author":"Min","year":"2018","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Caron, M., Bojanowski, P., Joulin, A., and Douze, M. (2018, January 8\u201314). Deep clustering for unsupervised learning of visual features. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01264-9_9"},{"key":"ref_18","unstructured":"Xie, J., Girshick, R., and Farhadi, A. (2016, January 20\u201322). Unsupervised deep embedding for clustering analysis. Proceedings of the International Conference on Machine Learning, New York, NY, USA."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Guo, X., Gao, L., Liu, X., and Yin, J. (2017, January 19\u201325). Improved deep embedded clustering with local structure preservation. Proceedings of the IJCAI\u2014International Joint Conference on Artificial Intelligence, Melbourne, Australia.","DOI":"10.24963\/ijcai.2017\/243"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.patcog.2018.05.019","article-title":"Discriminatively boosted image clustering with fully convolutional auto-encoders","volume":"83","author":"Li","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Hu, Z., Liu, W., Bian, J., Liu, X., and Liu, T.Y. (2018, January 5\u20139). Listening to chaotic whispers: A deep learning framework for news-oriented stock trend prediction. Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, Los Angeles, CA, USA.","DOI":"10.1145\/3159652.3159690"},{"key":"ref_22","unstructured":"Bai, S., Kolter, J.Z., and Koltun, V. (2018). An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv."},{"key":"ref_23","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","article-title":"Silhouettes: A graphical aid to the interpretation and validation of cluster analysis","volume":"20","author":"Rousseeuw","year":"1987","journal-title":"J. Comput. Appl. Math."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/03610927408827101","article-title":"A dendrite method for cluster analysis","volume":"3","author":"Harabasz","year":"1974","journal-title":"Commun. Stat. Theory Methods"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1109\/TPAMI.1979.4766909","article-title":"A cluster separation measure","volume":"2","author":"Davies","year":"1979","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/14\/11\/331\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:17:48Z","timestamp":1760145468000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/14\/11\/331"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,14]]},"references-count":26,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["fi14110331"],"URL":"https:\/\/doi.org\/10.3390\/fi14110331","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,14]]}}}