{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T18:19:49Z","timestamp":1774376389018,"version":"3.50.1"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"22","license":[{"start":{"date-parts":[[2023,8,22]],"date-time":"2023-08-22T00:00:00Z","timestamp":1692662400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,8,22]],"date-time":"2023-08-22T00:00:00Z","timestamp":1692662400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972424"],"award-info":[{"award-number":["61972424"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000646","name":"Japan Society for the Promotion of Science London","doi-asserted-by":"publisher","award":["JP19K20250"],"award-info":[{"award-number":["JP19K20250"]}],"id":[{"id":"10.13039\/501100000646","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000646","name":"Japan Society for the Promotion of Science London","doi-asserted-by":"publisher","award":["JP20H04174"],"award-info":[{"award-number":["JP20H04174"]}],"id":[{"id":"10.13039\/501100000646","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000646","name":"Japan Society for the Promotion of Science London","doi-asserted-by":"publisher","award":["JP22K11989"],"award-info":[{"award-number":["JP22K11989"]}],"id":[{"id":"10.13039\/501100000646","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Leading Initiative for Excellent Young Researchers"},{"name":"JST, PRESTO","award":["JPMJPR21P3"],"award-info":[{"award-number":["JPMJPR21P3"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,11]]},"DOI":"10.1007\/s10489-023-04859-z","type":"journal-article","created":{"date-parts":[[2023,8,22]],"date-time":"2023-08-22T01:02:01Z","timestamp":1692666121000},"page":"26297-26312","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A time series classification method combining graph embedding and the bag-of-patterns algorithm"],"prefix":"10.1007","volume":"53","author":[{"given":"Xiaoxuan","family":"Ma","sequence":"first","affiliation":[]},{"given":"Mengping","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Huan","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Rui","family":"Hou","sequence":"additional","affiliation":[]},{"given":"Mianxiong","family":"Dong","sequence":"additional","affiliation":[]},{"given":"Kaoru","family":"Ota","sequence":"additional","affiliation":[]},{"given":"Deze","family":"Zeng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,22]]},"reference":[{"issue":"11\u201312","key":"4859_CR1","doi-asserted-by":"publisher","first-page":"3211","DOI":"10.1007\/s10994-021-06057-9","volume":"110","author":"M Middlehurst","year":"2021","unstructured":"Middlehurst M, Large J, Flynn M, Lines J, Bostrom A, Bagnall A (2021) Hive-cote 2.0: a new meta ensemble for time series classification. Mach Learn 110(11\u201312):3211\u20133243","journal-title":"Mach Learn"},{"issue":"2","key":"4859_CR2","first-page":"276","volume":"10","author":"S Hadiyoso","year":"2019","unstructured":"Hadiyoso S, Aulia S, Rizal A (2019) One-lead electrocardiogram for biometric authentication using time series analysis and support vector machine. Int J Adv Comput Sci Appl 10(2):276\u2013283","journal-title":"Int J Adv Comput Sci Appl"},{"key":"4859_CR3","doi-asserted-by":"publisher","first-page":"113676","DOI":"10.1016\/j.eswa.2020.113676","volume":"161","author":"H Al-Hadeethi","year":"2020","unstructured":"Al-Hadeethi H, Abdulla S, Diykh M, Deo RC, Green JH (2020) Adaptive boost ls-svm classification approach for time-series signal classification in epileptic seizure diagnosis applications. Expert Syst Appl 161:113676","journal-title":"Expert Syst Appl"},{"key":"4859_CR4","doi-asserted-by":"crossref","unstructured":"Nihale S, Sharma S, Parashar L, Singh U (2020) Network traffic prediction using long short-term memory. In 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC), pp 338\u2013343. IEEE","DOI":"10.1109\/ICESC48915.2020.9156045"},{"key":"4859_CR5","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.jenvrad.2019.03.003","volume":"203","author":"ADK Tareen","year":"2019","unstructured":"Tareen ADK, Asim KM, Kearfott KJ, Rafique M, Nadeem MSA, Iqbal T, Rahman SU (2019) Automated anomalous behaviour detection in soil radon gas prior to earthquakes using computational intelligence techniques. J Environ Radioact 203:48\u201354","journal-title":"J Environ Radioact"},{"issue":"2","key":"4859_CR6","first-page":"338","volume":"40","author":"L Xu","year":"2018","unstructured":"Xu L, Wang J, Cui J, Hu M, Zhang K, Teng W (2018) Dynamic expression recognition based on dynamic time warping and active appearance model. J Electron Inf Technol 40(2):338\u2013345","journal-title":"J Electron Inf Technol"},{"issue":"3","key":"4859_CR7","doi-asserted-by":"publisher","first-page":"438","DOI":"10.1360\/N112018-00326","volume":"50","author":"M Shi","year":"2020","unstructured":"Shi M, Wang Z (2020) An interpretable gait recognition method based on time series features. Sci Sin Inf 50(3):438\u2013460","journal-title":"Sci Sin Inf"},{"key":"4859_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1687-1499-2012-118","volume":"2012","author":"X Luo","year":"2012","unstructured":"Luo X, Liu T, Liu J, Guo X, Wang G (2012) Design and implementation of a distributed fall detection system based on wireless sensor networks. EURASIP J Wirel Commun Netw 2012:1\u201313","journal-title":"EURASIP J Wirel Commun Netw"},{"issue":"2","key":"4859_CR9","first-page":"271","volume":"42","author":"R Shougang","year":"2021","unstructured":"Shougang R, Jingxu Z, Xingjian G (2021) Overview of feature extraction algorithms for time series. J Chin Comput Syst 42(2):271\u2013278","journal-title":"J Chin Comput Syst"},{"issue":"2","key":"4859_CR10","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1007\/s10844-012-0196-5","volume":"39","author":"J Lin","year":"2012","unstructured":"Lin J, Khade R, Li Y (2012) Rotation-invariant similarity in time series using bag-of-patterns representation. J Intell Inf Syst 39(2):287\u2013315","journal-title":"J Intell Inf Syst"},{"key":"4859_CR11","first-page":"3617","volume":"34","author":"Z Cheng","year":"2020","unstructured":"Cheng Z, Yang Y, Wang W, Wenjie Hu, Zhuang Y, Song G (2020) Time2graph: Revisiting time series modeling with dynamic shapelets. Proc AAAI Conf Artif Intell 34:3617\u20133624","journal-title":"Proc AAAI Conf Artif Intell"},{"issue":"9","key":"4859_CR12","doi-asserted-by":"publisher","first-page":"2231","DOI":"10.1016\/j.patcog.2010.09.022","volume":"44","author":"Y-S Jeong","year":"2011","unstructured":"Jeong Y-S, Jeong MK, Omitaomu OA (2011) Weighted dynamic time warping for time series classification. Pattern Recog 44(9):2231\u20132240","journal-title":"Pattern Recog"},{"issue":"2","key":"4859_CR13","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1109\/TPAMI.2008.76","volume":"31","author":"P-F Marteau","year":"2008","unstructured":"Marteau P-F (2008) Time warp edit distance with stiffness adjustment for time series matching. IEEE Trans Pattern Anal Mach Intell 31(2):306\u2013318","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"6","key":"4859_CR14","doi-asserted-by":"publisher","first-page":"1425","DOI":"10.1109\/TKDE.2012.88","volume":"25","author":"A Stefan","year":"2012","unstructured":"Stefan A, Athitsos V, Das G (2012) The move-split-merge metric for time series. IEEE Trans Knowl Data Eng 25(6):1425\u20131438","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"4859_CR15","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1007\/s10618-014-0361-2","volume":"29","author":"J Lines","year":"2015","unstructured":"Lines J, Bagnall A (2015) Time series classification with ensembles of elastic distance measures. Data Min Knowl Disc 29:565\u2013592","journal-title":"Data Min Knowl Disc"},{"issue":"3","key":"4859_CR16","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1007\/s10618-019-00617-3","volume":"33","author":"B Lucas","year":"2019","unstructured":"Lucas B, Shifaz A, Pelletier C, O\u2019Neill L, Zaidi N, Goethals B, Petitjean F, Webb GI (2019) Proximity forest: an effective and scalable distance-based classifier for time series. Data Min Knowl Discov 33(3):607\u2013635","journal-title":"Data Min Knowl Discov"},{"issue":"3","key":"4859_CR17","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1007\/s10618-020-00679-8","volume":"34","author":"A Shifaz","year":"2020","unstructured":"Shifaz A, Pelletier C, Petitjean F, Webb GI (2020) TS-CHIEF: a scalable and accurate forest algorithm for time series classification. Data Min Knowl Discov 34(3):742\u2013775","journal-title":"Data Min Knowl Discov"},{"key":"4859_CR18","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1007\/s10618-017-0519-9","volume":"32","author":"C-CM Yeh","year":"2018","unstructured":"Yeh C-CM, Zhu Y, Ulanova L, Begum N, Ding Y, Dau HA, Zimmerman Z, Silva DF, Mueen A, Keogh E (2018) Time series joins, motifs, discords and shapelets: a unifying view that exploits the matrix profile. Data Min Knowl Discov 32:83\u2013123","journal-title":"Data Min Knowl Discov"},{"key":"4859_CR19","doi-asserted-by":"publisher","first-page":"949","DOI":"10.1007\/s10618-019-00668-6","volume":"34","author":"Y Zhu","year":"2020","unstructured":"Zhu Y, Gharghabi S, Silva DF, Dau HA, Yeh C-CM, Senobari NS, Almaslukh A, Kamgar K, Zimmerman Z, Funning G et al (2020) The Swiss army knife of time series data mining: ten useful things you can do with the matrix profile and ten lines of code. Data Min Knowl Discov 34:949\u2013979","journal-title":"Data Min Knowl Discov"},{"issue":"1","key":"4859_CR20","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1007\/s10618-010-0179-5","volume":"22","author":"L Ye","year":"2011","unstructured":"Ye L, Keogh E (2011) Time series shapelets: a novel technique that allows accurate, interpretable and fast classification. Data Min Knowl Disc 22(1):149\u2013182","journal-title":"Data Min Knowl Disc"},{"key":"4859_CR21","first-page":"668","volume-title":"Fast-shapelets: A fast algorithm for discovering robust time series shapelets","author":"T Rakthanmanon","year":"2013","unstructured":"Rakthanmanon T, Keogh E (2013) Fast-shapelets: a fast algorithm for discovering robust time series shapelets. In Proceedings of 11th SIAM international conference on data mining, pp 668\u2013676"},{"issue":"4","key":"4859_CR22","doi-asserted-by":"publisher","first-page":"851","DOI":"10.1007\/s10618-013-0322-1","volume":"28","author":"J Hills","year":"2014","unstructured":"Hills J, Lines J, Baranauskas E, Mapp J, Bagnall A (2014) Classification of time series by shapelet transformation. Data Min Knowl Disc 28(4):851\u2013881","journal-title":"Data Min Knowl Disc"},{"key":"4859_CR23","doi-asserted-by":"crossref","unstructured":"Grabocka J, Schilling N, Wistuba M, Schmidt-Thieme L (2014) Learning time-series shapelets. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 392\u2013401","DOI":"10.1145\/2623330.2623613"},{"issue":"3","key":"4859_CR24","doi-asserted-by":"publisher","first-page":"1149","DOI":"10.1109\/TKDE.2020.2995870","volume":"34","author":"G Li","year":"2020","unstructured":"Li G, Choi B, Jianliang X, Bhowmick SS, Chun K-P, Wong GL-H (2020) Efficient shapelet discovery for time series classification. IEEE Trans Knowl Data Eng 34(3):1149\u20131163","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"2","key":"4859_CR25","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/s10618-007-0064-z","volume":"15","author":"J Lin","year":"2007","unstructured":"Lin J, Keogh E, Wei Li, Lonardi S (2007) Experiencing sax: a novel symbolic representation of time series. Data Min Knowl Discov 15(2):107\u2013144","journal-title":"Data Min Knowl Discov"},{"key":"4859_CR26","doi-asserted-by":"crossref","unstructured":"Sch\u00e4fer P, H\u00f6gqvist M (2012) Sfa: a symbolic fourier approximation and index for similarity search in high dimensional datasets. In Proceedings of the 15th international conference on extending database technology, pp 516\u2013527","DOI":"10.1145\/2247596.2247656"},{"key":"4859_CR27","doi-asserted-by":"crossref","unstructured":"Senin P, Malinchik S (2013) Sax-vsm: Interpretable time series classification using sax and vector space model. In 2013 IEEE 13th international conference on data mining, pp 1175\u20131180. IEEE","DOI":"10.1109\/ICDM.2013.52"},{"issue":"6","key":"4859_CR28","doi-asserted-by":"publisher","first-page":"1505","DOI":"10.1007\/s10618-014-0377-7","volume":"29","author":"P Sch\u00e4fer","year":"2015","unstructured":"Sch\u00e4fer P (2015) The boss is concerned with time series classification in the presence of noise. Data Min Knowl Disc 29(6):1505\u20131530","journal-title":"Data Min Knowl Disc"},{"key":"4859_CR29","doi-asserted-by":"crossref","unstructured":"Sch\u00e4fer P, Leser U (2017) Fast and accurate time series classification with weasel. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp 637\u2013646","DOI":"10.1145\/3132847.3132980"},{"key":"4859_CR30","doi-asserted-by":"crossref","unstructured":"Nguyen TL, Ifrim G (2023) Fast time series classification with random symbolic subsequences. In Advanced Analytics and Learning on Temporal Data: 7th ECML PKDD Workshop, AALTD 2022, Grenoble, France, September 19\u201323, 2022, Revised Selected Papers, pp 50\u201365. Springer","DOI":"10.1007\/978-3-031-24378-3_4"},{"key":"4859_CR31","doi-asserted-by":"crossref","unstructured":"Middlehurst M, Large J, Cawley G, Bagnall A (2021) The temporal dictionary ensemble (TDE) classifier for time series classification. In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2020, Ghent, Belgium, September 14\u201318, 2020, Proceedings, Part I, pp 660\u2013676. Springer","DOI":"10.1007\/978-3-030-67658-2_38"},{"issue":"5","key":"4859_CR32","doi-asserted-by":"publisher","first-page":"1073","DOI":"10.3233\/IDA-184333","volume":"23","author":"J Large","year":"2019","unstructured":"Large J, Bagnall A, Malinowski S, Tavenard R (2019) On time series classification with dictionary-based classifiers. Intell Data Anal 23(5):1073\u20131089","journal-title":"Intell Data Anal"},{"key":"4859_CR33","unstructured":"ZhuoYa J (2021) Research on Key Techniques of Discriminative Patterns Discovery and Classification Methods of Time Series. PhD thesis, Beijing Jiaotong University"},{"key":"4859_CR34","doi-asserted-by":"crossref","unstructured":"Wang Z, Yan W, Oates T (2017) Time series classification from scratch with deep neural networks: A strong baseline. In 2017 International joint conference on neural networks (IJCNN), pp 1578\u20131585. IEEE","DOI":"10.1109\/IJCNN.2017.7966039"},{"key":"4859_CR35","unstructured":"Cui Z, Chen W, Chen Y (2016) Multi-scale convolutional neural networks for time series classification. arXiv preprint arXiv:1603.06995"},{"key":"4859_CR36","doi-asserted-by":"publisher","first-page":"1662","DOI":"10.1109\/ACCESS.2017.2779939","volume":"6","author":"F Karim","year":"2017","unstructured":"Karim F, Majumdar S, Darabi H, Chen S (2017) Lstm fully convolutional networks for time series classification. IEEE Access 6:1662\u20131669","journal-title":"IEEE Access"},{"issue":"6","key":"4859_CR37","doi-asserted-by":"publisher","first-page":"1936","DOI":"10.1007\/s10618-020-00710-y","volume":"34","author":"H Ismail Fawaz","year":"2020","unstructured":"Ismail Fawaz H, Lucas B, Forestier G, Pelletier C, Schmidt DF, Weber J, Webb GI, Idoumghar L, Muller P-A, Petitjean F (2020) Inceptiontime: Finding alexnet for time series classification. Data Min Knowl Disc 34(6):1936\u20131962","journal-title":"Data Min Knowl Disc"},{"key":"4859_CR38","unstructured":"Conde MV, Shubham K, Agnihotri P, Movva ND, Bessenyei S (2021) Weakly-Supervised Classification and Detection of Bird Sounds in the Wild. A BirdCLEF 2021 Solution. In Conference and Labs of the Evaluation Forum"},{"key":"4859_CR39","doi-asserted-by":"publisher","first-page":"1615","DOI":"10.32604\/cmc.2020.09938","volume":"9","author":"Y Zhao","year":"2020","unstructured":"Zhao Y, Cheng J, Zhang P, Peng X (2020) ECG classification using deep CNN improved by wavelet transform. Comput Mater Continua 9:1615\u20131628","journal-title":"Comput Mater Continua"},{"key":"4859_CR40","doi-asserted-by":"crossref","unstructured":"Wang J, Wang Z, Li J, Wu J (2018) Multilevel wavelet decomposition network for interpretable time series analysis. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 2437\u20132446","DOI":"10.1145\/3219819.3220060"},{"issue":"5","key":"4859_CR41","doi-asserted-by":"publisher","first-page":"1273","DOI":"10.1007\/s10618-015-0441-y","volume":"30","author":"P Sch\u00e4fer","year":"2016","unstructured":"Sch\u00e4fer P (2016) Scalable time series classification. Data Min Knowl Disc 30(5):1273\u20131298","journal-title":"Data Min Knowl Disc"},{"issue":"10","key":"4859_CR42","first-page":"3216","volume":"31","author":"Z Wei","year":"2020","unstructured":"Wei Z, ZhiHai W, JiDong Y, ShiLei H (2020) Time series discriminative feature dictionary construction algorithm. J Softw 31(10):3216\u20133237","journal-title":"J Softw"},{"issue":"9","key":"4859_CR43","doi-asserted-by":"publisher","first-page":"1616","DOI":"10.1109\/TKDE.2018.2807452","volume":"30","author":"H Cai","year":"2018","unstructured":"Cai H, Zheng VW, Chang KC-C (2018) A comprehensive survey of graph embedding: Problems, techniques, and applications. IEEE Trans Knowl Data Eng 30(9):1616\u20131637","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"4859_CR44","doi-asserted-by":"crossref","unstructured":"Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 701\u2013710","DOI":"10.1145\/2623330.2623732"},{"issue":"6","key":"4859_CR45","doi-asserted-by":"publisher","first-page":"1293","DOI":"10.1109\/JAS.2019.1911747","volume":"6","author":"HA Dau","year":"2019","unstructured":"Dau HA, Bagnall A, Kamgar K, Yeh C-CM, Zhu Y, Gharghabi S, Ratanamahatana CA, Keogh E (2019) The ucr time series archive. IEEE\/CAA J Autom Sin 6(6):1293\u20131305","journal-title":"IEEE\/CAA J Autom Sin"},{"issue":"3","key":"4859_CR46","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1007\/s10618-016-0483-9","volume":"31","author":"A Bagnall","year":"2017","unstructured":"Bagnall A, Lines J, Bostrom A, Large J, Keogh E (2017) The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Min Knowl Disc 31(3):606\u2013660","journal-title":"Data Min Knowl Disc"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04859-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-023-04859-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04859-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,25]],"date-time":"2023-10-25T23:04:46Z","timestamp":1698275086000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-023-04859-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,22]]},"references-count":46,"journal-issue":{"issue":"22","published-print":{"date-parts":[[2023,11]]}},"alternative-id":["4859"],"URL":"https:\/\/doi.org\/10.1007\/s10489-023-04859-z","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,22]]},"assertion":[{"value":"30 June 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 August 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"There are no potential conflicts of interset to declare.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interest"}}]}}