{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T10:24:41Z","timestamp":1779186281248,"version":"3.51.4"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,2,7]],"date-time":"2024-02-07T00:00:00Z","timestamp":1707264000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,2,7]],"date-time":"2024-02-07T00:00:00Z","timestamp":1707264000000},"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":["No. 2022YFE0200400"],"award-info":[{"award-number":["No. 2022YFE0200400"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["No. 2022JBMC011"],"award-info":[{"award-number":["No. 2022JBMC011"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The deep forest presents a novel approach that yields competitive performance when compared to deep neural networks. Nevertheless, there are limited studies on the application of deep forest to time series classification (TSC) tasks, and the direct use of deep forest cannot effectively capture the relevant characteristics of time series. For that, this paper proposes time series cascade forest (TSCF), a model specifically designed for TSC tasks. TSCF relies on four base classifiers, i.e., random forest, completely random forest, random shapelet forest, and diverse representation canonical interval forest, allowing for feature learning on the original data from three granularities: point, subsequence, and summary statistics calculated based on intervals. The major contribution of this work, is to define an ensemble and deep classifier that significantly outperforms the individual classifiers and the original deep forest. Experimental results show that TSCF outperforms other forest-based algorithms for solving TSC problems.<\/jats:p>","DOI":"10.1007\/s11063-024-11531-1","type":"journal-article","created":{"date-parts":[[2024,2,7]],"date-time":"2024-02-07T05:02:13Z","timestamp":1707282133000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["TSCF: An Improved Deep Forest Model for Time Series Classification"],"prefix":"10.1007","volume":"56","author":[{"given":"Mingxin","family":"Dai","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jidong","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haiyang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinfeng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,2,7]]},"reference":[{"key":"11531_CR1","unstructured":"Pazzani MJ, et\u00a0al (2001) Derivative dynamic time warping. In: Proceedings of the 2001 SIAM international conference on data mining. Society for Industrial and Applied Mathematics"},{"key":"11531_CR2","doi-asserted-by":"crossref","unstructured":"Yuan J, Lin Q, Zhang W, et\u00a0al (2019) Locally slope-based dynamic time warping for time series classification. In: Proceedings of the 28th ACM international conference on information and knowledge management, pp 1713\u20131722","DOI":"10.1145\/3357384.3357917"},{"issue":"9","key":"11531_CR3","doi-asserted-by":"publisher","first-page":"2231","DOI":"10.1016\/j.patcog.2010.09.022","volume":"44","author":"YS Jeong","year":"2011","unstructured":"Jeong YS, Jeong MK, Omitaomu OA (2011) Weighted dynamic time warping for time series classification. Pattern Recogn 44(9):2231\u20132240","journal-title":"Pattern Recogn"},{"key":"11531_CR4","doi-asserted-by":"crossref","unstructured":"Ye L, Keogh E (2009) Time series shapelets: a new primitive for data mining. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, pp 947\u2013956","DOI":"10.1145\/1557019.1557122"},{"key":"11531_CR5","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1016\/j.ins.2013.02.030","volume":"239","author":"H Deng","year":"2013","unstructured":"Deng H, Runger G, Tuv E et al (2013) A time series forest for classification and feature extraction. Inf Sci 239:142\u2013153","journal-title":"Inf Sci"},{"key":"11531_CR6","doi-asserted-by":"crossref","unstructured":"Middlehurst M, Large J, Bagnall A (2020) The canonical interval forest (CIF) classifier for time series classification. In: 2020 IEEE international conference on big data (Big Data), IEEE, pp 188\u2013195","DOI":"10.1109\/BigData50022.2020.9378424"},{"key":"11531_CR7","doi-asserted-by":"publisher","first-page":"1053","DOI":"10.1007\/s10618-016-0473-y","volume":"30","author":"I Karlsson","year":"2016","unstructured":"Karlsson I, Papapetrou P, Bostr\u00f6m H (2016) Generalized random shapelet forests. Data Min Knowl Disc 30:1053\u20131085","journal-title":"Data Min Knowl Disc"},{"issue":"6","key":"11531_CR8","doi-asserted-by":"publisher","first-page":"1821","DOI":"10.1007\/s10618-019-00647-x","volume":"33","author":"CH Lubba","year":"2019","unstructured":"Lubba CH, Sethi SS, Knaute P et al (2019) catch22: canonical time-series characteristics: Selected through highly comparative time-series analysis. Data Min Knowl Disc 33(6):1821\u20131852","journal-title":"Data Min Knowl Disc"},{"issue":"11\u201312","key":"11531_CR9","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 et al (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":"1","key":"11531_CR10","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1093\/nsr\/nwy108","volume":"6","author":"ZH Zhou","year":"2019","unstructured":"Zhou ZH, Feng J (2019) Deep forest. Natl Sci Rev 6(1):74\u201386","journal-title":"Natl Sci Rev"},{"issue":"6","key":"11531_CR11","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":"11531_CR12","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"},{"key":"11531_CR13","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1016\/j.patcog.2017.09.020","volume":"74","author":"J Zhao","year":"2018","unstructured":"Zhao J, Itti L (2018) shapedtw: shape dynamic time warping. Pattern Recogn 74:171\u2013184","journal-title":"Pattern Recogn"},{"key":"11531_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10115-021-01630-z","volume":"64","author":"J Yuan","year":"2022","unstructured":"Yuan J, Shi M, Wang Z et al (2022) Random pairwise shapelets forest: an effective classifier for time series. Knowl Inf Syst 64:1\u201332","journal-title":"Knowl Inf Syst"},{"key":"11531_CR15","doi-asserted-by":"crossref","unstructured":"Grabocka J, Schilling N, Wistuba M, et\u00a0al (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"},{"key":"11531_CR16","doi-asserted-by":"publisher","first-page":"2317","DOI":"10.1016\/j.neucom.2017.11.002","volume":"275","author":"Z Zhang","year":"2018","unstructured":"Zhang Z, Zhang H, Wen Y et al (2018) Discriminative extraction of features from time series. Neurocomputing 275:2317\u20132328","journal-title":"Neurocomputing"},{"key":"11531_CR17","doi-asserted-by":"crossref","unstructured":"Lines J, Davis LM, Hills J, et\u00a0al (2012) A shapelet transform for time series classification. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining, pp 289\u2013297","DOI":"10.1145\/2339530.2339579"},{"key":"11531_CR18","doi-asserted-by":"crossref","unstructured":"Lines J, Taylor S, Bagnall A (2016) Hive-cote: The hierarchical vote collective of transformation-based ensembles for time series classification. In: 2016 IEEE 16th international conference on data mining (ICDM), IEEE, pp 1041\u20131046","DOI":"10.1109\/ICDM.2016.0133"},{"issue":"5","key":"11531_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3182382","volume":"12","author":"J Lines","year":"2018","unstructured":"Lines J, Taylor S, Bagnall A (2018) Time series classification with hive-cote: the hierarchical vote collective of transformation-based ensembles. ACM Trans Knowl Discov Data 12(5):1\u201335","journal-title":"ACM Trans Knowl Discov Data"},{"key":"11531_CR20","doi-asserted-by":"crossref","unstructured":"Cabello N, Naghizade E, Qi J, et\u00a0al (2020) Fast and accurate time series classification through supervised interval search. In: 2020 IEEE international conference on data mining (ICDM), IEEE, pp 948\u2013953","DOI":"10.1109\/ICDM50108.2020.00107"},{"issue":"11","key":"11531_CR21","doi-asserted-by":"publisher","first-page":"2796","DOI":"10.1109\/TPAMI.2013.72","volume":"35","author":"MG Baydogan","year":"2013","unstructured":"Baydogan MG, Runger G, Tuv E (2013) A bag-of-features framework to classify time series. IEEE Trans Pattern Anal Mach Intell 35(11):2796\u20132802","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"11531_CR22","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1007\/s10618-015-0425-y","volume":"30","author":"MG Baydogan","year":"2016","unstructured":"Baydogan MG, Runger G (2016) Time series representation and similarity based on local autopatterns. Data Min Knowl Disc 30:476\u2013509","journal-title":"Data Min Knowl Disc"},{"key":"11531_CR23","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 29:1505\u20131530","journal-title":"Data Min Knowl Disc"},{"key":"11531_CR24","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"},{"issue":"5","key":"11531_CR25","doi-asserted-by":"publisher","first-page":"1454","DOI":"10.1007\/s10618-020-00701-z","volume":"34","author":"A Dempster","year":"2020","unstructured":"Dempster A, Petitjean F, Webb GI (2020) Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Min Knowl Disc 34(5):1454\u20131495","journal-title":"Data Min Knowl Disc"},{"issue":"3","key":"11531_CR26","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 et al (2020) Ts-chief: a scalable and accurate forest algorithm for time series classification. Data Min Knowl Disc 34(3):742\u2013775","journal-title":"Data Min Knowl Disc"},{"key":"11531_CR27","unstructured":"Middlehurst M, Sch\u00e4fer P, Bagnall A (2023) Bake off redux: a review and experimental evaluation of recent time series classification algorithms. arXiv preprint arXiv:2304.13029"},{"key":"11531_CR28","unstructured":"Le\u00a0Guennec A, Malinowski S, Tavenard R (2016) Data augmentation for time series classification using convolutional neural networks. In: ECML\/PKDD workshop on advanced analytics and learning on temporal data"},{"key":"11531_CR29","unstructured":"Cui Z, Chen W, Chen Y (2016) Multi-scale convolutional neural networks for time series classification. arXiv preprint arXiv:1603.06995"},{"key":"11531_CR30","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, et\u00a0al (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"11531_CR31","doi-asserted-by":"crossref","unstructured":"Tanisaro P, Heidemann G (2016) Time series classification using time warping invariant echo state networks. In: 2016 15th IEEE international conference on machine learning and applications (ICMLA), IEEE, pp 831\u2013836","DOI":"10.1109\/ICMLA.2016.0149"},{"issue":"6","key":"11531_CR32","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 et al (2020) Inceptiontime: finding alexnet for time series classification. Data Min Knowl Disc 34(6):1936\u20131962","journal-title":"Data Min Knowl Disc"},{"issue":"4","key":"11531_CR33","doi-asserted-by":"publisher","first-page":"917","DOI":"10.1007\/s10618-019-00619-1","volume":"33","author":"H Ismail Fawaz","year":"2019","unstructured":"Ismail Fawaz H, Forestier G, Weber J et al (2019) Deep learning for time series classification: a review. Data Min Knowl Disc 33(4):917\u2013963","journal-title":"Data Min Knowl Disc"},{"issue":"3","key":"11531_CR34","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 et al (2019) Proximity forest: an effective and scalable distance-based classifier for time series. Data Min Knowl Disc 33(3):607\u2013635","journal-title":"Data Min Knowl Disc"},{"issue":"1","key":"11531_CR35","first-page":"2653","volume":"18","author":"A Benavoli","year":"2017","unstructured":"Benavoli A, Corani G, Dem\u0161ar J et al (2017) Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis. J Mach Learn Res 18(1):2653\u20132688","journal-title":"J Mach Learn Res"},{"key":"11531_CR36","first-page":"1","volume":"7","author":"J Dem\u0161ar","year":"2006","unstructured":"Dem\u0161ar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1\u201330","journal-title":"J Mach Learn Res"},{"issue":"1","key":"11531_CR37","doi-asserted-by":"publisher","first-page":"162","DOI":"10.21629\/JSEE.2017.01.18","volume":"28","author":"B Zhao","year":"2017","unstructured":"Zhao B, Lu H, Chen S et al (2017) Convolutional neural networks for time series classification. J Syst Eng Electron 28(1):162\u2013169","journal-title":"J Syst Eng Electron"},{"key":"11531_CR38","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), IEEE, pp 1578\u20131585","DOI":"10.1109\/IJCNN.2017.7966039"},{"issue":"11","key":"11531_CR39","first-page":"2579","volume":"9","author":"L Van der Maaten","year":"2008","unstructured":"Van der Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9(11):2579\u20132605","journal-title":"J Mach Learn Res"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-024-11531-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-024-11531-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-024-11531-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,29]],"date-time":"2024-02-29T20:09:21Z","timestamp":1709237361000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-024-11531-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,7]]},"references-count":39,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["11531"],"URL":"https:\/\/doi.org\/10.1007\/s11063-024-11531-1","relation":{},"ISSN":["1573-773X"],"issn-type":[{"value":"1573-773X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,7]]},"assertion":[{"value":"8 January 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 February 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}],"article-number":"13"}}