{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T18:02:13Z","timestamp":1767981733453,"version":"3.49.0"},"reference-count":36,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,3,19]],"date-time":"2021-03-19T00:00:00Z","timestamp":1616112000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2017YFB0304100"],"award-info":[{"award-number":["2017YFB0304100"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1908213"],"award-info":[{"award-number":["U1908213"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["N182303037"],"award-info":[{"award-number":["N182303037"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Foundation of Northeastern University at Qinhuangdao","award":["XNB201803"],"award-info":[{"award-number":["XNB201803"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>In many industrial domains, there is a significant interest in obtaining temporal relationships among multiple variables in time-series data, given that such relationships play an auxiliary role in decision making. However, when transactions occur frequently only for a period of time, it is difficult for a traditional time-series association rules mining algorithm (TSARM) to identify this kind of relationship. In this paper, we propose a new TSARM framework and a novel algorithm named TSARM-UDP. A TSARM mining framework is used to mine time-series association rules (TSARs) and an up-to-date pattern (UDP) is applied to discover rare patterns that only appear in a period of time. Based on the up-to-date pattern mining, the proposed TSAR-UDP method could extract temporal relationship rules with better generality. The rules can be widely used in the process industry, the stock market, etc. Experiments are then performed on the public stock data and real blast furnace data to verify the effectiveness of the proposed algorithm. We compare our algorithm with three state-of-the-art algorithms, and the experimental results show that our algorithm can provide greater efficiency and interpretability in TSARs and that it has good prospects.<\/jats:p>","DOI":"10.3390\/e23030365","type":"journal-article","created":{"date-parts":[[2021,3,19]],"date-time":"2021-03-19T03:41:21Z","timestamp":1616125281000},"page":"365","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["TSARM-UDP: An Efficient Time Series Association Rules Mining Algorithm Based on Up-to-Date Patterns"],"prefix":"10.3390","volume":"23","author":[{"given":"Qiang","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4645-5348","authenticated-orcid":false,"given":"Qing","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer and Communication Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Deshui","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Computer and Communication Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yinghua","family":"Han","sequence":"additional","affiliation":[{"name":"College of Computer and Communication Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.aei.2016.02.003","article-title":"Fast algorithms for mining high-utility itemsets with various discount strategies","volume":"30","author":"Jerry","year":"2016","journal-title":"Adv. Eng. Inf."},{"key":"ref_2","unstructured":"Galit, S., Bruce, P.C., Yahav, I., Patel, N.R., and Lichtendahl, K.C. (2017). Data Mining for Business Analytics: Concepts, Techniques, and Applications in R, John Wiley & Sons."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1093\/bioinformatics\/17.6.495","article-title":"Aligning gene expression time-series with time warping algorithms","volume":"17","author":"John","year":"2001","journal-title":"Bioinformatics"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1166\/jmihi.2018.2284","article-title":"Optimal channel selection on Electroencephalography (EEG) device data using feature re-ranking and rough set theory on eye state classification problem","volume":"8","author":"Mridu","year":"2018","journal-title":"J. Med. Imaging Health Inform."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3398202","article-title":"On Scalability of Association-rule-based recommendation: A unified distributed-computing framework","volume":"14","author":"Zhiang","year":"2020","journal-title":"ACM Trans. Web."},{"key":"ref_6","first-page":"102","article-title":"Association rule mining for the ordered placement of traditional Chinese medicine containers: An experimental study","volume":"99","author":"Tsai","year":"2020","journal-title":"Medicine"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1111\/1468-2354.00061","article-title":"Time series analysis of deregulatory dynamics and technical efficiency: The case of the US airline industry","volume":"41","author":"Alam","year":"2000","journal-title":"Int. Econ. Rev."},{"key":"ref_8","unstructured":"Fu-lai, C., Fu, T.C., Luk, R., and Ng, V. (2002, January 9\u201312). Evolutionary time-series segmentation for stock data mining. Proceedings of the IEEE Congress on Evolutionary Computation, ICDM 2002, Maebashi City, Japan."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.knosys.2013.09.003","article-title":"Web usage mining with evolutionary extraction of temporal fuzzy association rules","volume":"54","author":"Matthews","year":"2013","journal-title":"Knowl.-Based Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1109\/TKDE.2018.2881675","article-title":"Sequence pattern mining with variables","volume":"32","author":"Okolica","year":"2018","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_11","unstructured":"Khosravy, M., and Gupta, N. (2004). Practical Genetic Algorithms, Wiley-IEEE Publication."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1007\/s10618-007-0077-7","article-title":"Data mining with temporal abstractions: Learning rules from time-series","volume":"15","author":"Sacchi","year":"2007","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.asoc.2016.01.008","article-title":"Mining fuzzy temporal association rules by item lifespans","volume":"41","author":"Chen","year":"2016","journal-title":"Appl. Soft. Comput."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1016\/j.asoc.2011.08.006","article-title":"Fuzzy data mining for time-series data","volume":"12","author":"Chen","year":"2012","journal-title":"Appl. Soft. Comput."},{"key":"ref_15","first-page":"2278","article-title":"A recent survey on incremental temporal association rule mining","volume":"13","author":"Shirsath","year":"2013","journal-title":"IJITEE"},{"key":"ref_16","first-page":"1","article-title":"The construction of hierarchical network model and wireless activation diffusion optimization model in English teaching","volume":"20","author":"Yang","year":"2020","journal-title":"EURASIP J. Wirel. Commun. Netw."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"112950","DOI":"10.1016\/j.eswa.2019.112950","article-title":"SAX-ARM: Deviant event pattern discovery from multivariate time-series using symbolic aggregate approximation and association rule mining","volume":"141","author":"Park","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Agrawal, R., Imieli\u0144ski, T., and Swami, A. (1993, January 26\u201328). Mining association rules between sets of items in large databases. Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, DC, USA.","DOI":"10.1145\/170035.170072"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1109\/TEM.2017.2712606","article-title":"A new methodology for mining frequent itemsets on temporal data","volume":"64","author":"Ghorbani","year":"2017","journal-title":"IEEE Trans. Eng. Manag."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Mantovani, M., Combi, C., and Zeggiotti, M. (2019, January 10\u201313). Discovering and analyzing trend-event patterns on clinical data. Proceedings of the 2019 IEEE International Conference on Healthcare Informatics, Beijing, China.","DOI":"10.1109\/ICHI.2019.8904774"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Combi, C., and Sabaini, A. (2013, January 7\u20139). Extraction, analysis, and visualization of temporal association rules from interval-based clinical data. Proceedings of the 2013 Conference on Artificial Intelligence in Medicine in Europe, Murcia, Spain.","DOI":"10.1007\/978-3-642-38326-7_35"},{"key":"ref_22","first-page":"10","article-title":"Research on multiple time-series inter-transaction association analysi","volume":"41","author":"Qin","year":"2005","journal-title":"Comput. Eng. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ruan, G., Zhang, H., and Plale, B. (2014, January 27\u201330). Parallel and quantitative sequential pattern mining for large-scale interval-based temporal data. Proceedings of the 2014 IEEE International Conference on Big Data, Sydney, Australia.","DOI":"10.1109\/BigData.2014.7004410"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3321486","article-title":"A unified framework for frequent sequence mining with subsequence constraints","volume":"44","author":"Beedkar","year":"2019","journal-title":"ACM Trans. Database Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.jbi.2011.11.005","article-title":"Querying temporal clinical databases on granular trends","volume":"45","author":"Combi","year":"2012","journal-title":"J. Biomed. Inform."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"9747","DOI":"10.1016\/j.eswa.2009.02.029","article-title":"An effective mining approach for up-to-date patterns","volume":"36","author":"Hong","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1016\/j.asoc.2017.09.013","article-title":"Mining temporal association rules with frequent itemsets tree","volume":"62","author":"Wang","year":"2018","journal-title":"Appl. Soft. Comput."},{"key":"ref_28","first-page":"591","article-title":"Temporal association rules mining algorithm based on frequent item sets tree","volume":"33","author":"Wang","year":"2018","journal-title":"Control Decis."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"648","DOI":"10.1016\/j.aei.2015.06.002","article-title":"Efficient algorithms for mining up-to-date high-utility patterns","volume":"29","author":"Lin","year":"2015","journal-title":"Adv. Eng. Inform."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Lin, C.-W., Hong, T.-P., and Lu, W.-H. (2009, January 14\u201316). Mining up-to-date knowledge based on tree structures. Proceedings of the 2009 International Conference of Soft Computing and Pattern Recognition, Dalian, China.","DOI":"10.1109\/SoCPaR.2009.36"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"15143","DOI":"10.1016\/j.eswa.2011.05.090","article-title":"Temporal data mining with up-to-date pattern trees","volume":"38","author":"Lin","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Namaki, M.H., Wu, Y., Song, Q., Lin, P., and Ge, T. (2017, January 3\u20137). Discovering graph temporal association rules. Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, Singapore.","DOI":"10.1145\/3132847.3133014"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1007\/s10044-018-0759-3","article-title":"Rare association rule mining from incremental databases","volume":"23","author":"Borah","year":"2020","journal-title":"Pattern Anal. Appl."},{"key":"ref_34","unstructured":"(2020, July 22). ISTANBUL STOCK EXCHANGE Data Set. Available online: https:\/\/archive.ics.uci.edu\/ml\/d."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/j.knosys.2018.07.031","article-title":"LTARM: A novel temporal association rule mining method to understand toxicities in a routine cancer treatment","volume":"161","author":"Nguyen","year":"2018","journal-title":"Knowl.-Based Syst."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.eswa.2018.07.006","article-title":"Eliciting and utilising knowledge for security event log analysis: An association rule mining and automated planning approach","volume":"113","author":"Khan","year":"2018","journal-title":"Expert Syst. Appl."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/23\/3\/365\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:37:55Z","timestamp":1760161075000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/23\/3\/365"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,19]]},"references-count":36,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,3]]}},"alternative-id":["e23030365"],"URL":"https:\/\/doi.org\/10.3390\/e23030365","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,19]]}}}