{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T08:09:06Z","timestamp":1770883746955,"version":"3.50.1"},"reference-count":58,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,8,17]],"date-time":"2021-08-17T00:00:00Z","timestamp":1629158400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Technology and Innovation Commission of Shenzhen Munici-pality","award":["JCYJ20190806112210067"],"award-info":[{"award-number":["JCYJ20190806112210067"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Time series classification (TSC) is a significant problem in data mining with several applications in different domains. Mining different distinguishing features is the primary method. One promising method is algorithms based on the morphological structure of time series, which are interpretable and accurate. However, existing structural feature-based algorithms, such as time series forest (TSF) and shapelet traverse, all features through many random combinations, which means that a lot of training time and computing resources are required to filter meaningless features, important distinguishing information will be ignored. To overcome this problem, in this paper, we propose a perceptual features-based framework for TSC. We are inspired by how humans observe time series and realize that there are usually only a few essential points that need to be remembered for a time series. Although the complex time series has a lot of details, a small number of data points is enough to describe the shape of the entire sample. First, we use the improved perceptually important points (PIPs) to extract key points and use them as the basis for time series segmentation to obtain a combination of interval-level and point-level features. Secondly, we propose a framework to explore the effects of perceptual structural features combined with decision trees (DT), random forests (RF), and gradient boosting decision trees (GBDT) on TSC. The experimental results on the UCR datasets show that our work has achieved leading accuracy, which is instructive for follow-up research.<\/jats:p>","DOI":"10.3390\/e23081059","type":"journal-article","created":{"date-parts":[[2021,8,17]],"date-time":"2021-08-17T03:43:04Z","timestamp":1629171784000},"page":"1059","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["PFC: A Novel Perceptual Features-Based Framework for Time Series Classification"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7655-7636","authenticated-orcid":false,"given":"Shaocong","family":"Wu","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9003-4252","authenticated-orcid":false,"given":"Xiaolong","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4054-9925","authenticated-orcid":false,"given":"Mengxia","family":"Liang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China"}]},{"given":"Dingming","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,17]]},"reference":[{"key":"ref_1","unstructured":"Wei, W.W. (2006). Time series analysis. The Oxford Handbook of Quantitative Methods in Psychology: Volume 2, Oxford University Press."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1007\/s10618-016-0483-9","article-title":"The great time series classification bake off: A review and experimental evaluation of recent algorithmic advances","volume":"31","author":"Bagnall","year":"2016","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1007\/s10618-019-00619-1","article-title":"Deep learning for time series classification: A review","volume":"33","author":"Fawaz","year":"2019","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Geurts, P. (2001). Pattern Extraction for Time Series Classification. Principles of Data Mining and Knowledge Discovery, Springer.","DOI":"10.1007\/3-540-44794-6_10"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Elhoseiny, M., Huang, S., and Elgammal, A. (2015, January 27\u201330). Weather classification with deep convolutional neural networks. Proceedings of the 2015 IEEE International Conference on Image Processing (ICIP), Quebec City, QC, Canada.","DOI":"10.1109\/ICIP.2015.7351424"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1306","DOI":"10.1109\/JAS.2019.1911774","article-title":"Classification of short time series in early Parkinsons disease with deep learning of fuzzy recurrence plots","volume":"6","author":"Pham","year":"2019","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.cmpb.2017.04.007","article-title":"An automatic non-invasive method for Parkinson\u2019s disease classification","volume":"145","author":"Joshi","year":"2017","journal-title":"Comput. Methods Progr. Biomed."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1293","DOI":"10.1109\/JAS.2019.1911747","article-title":"The UCR time series archive","volume":"6","author":"Dau","year":"2019","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Keogh, E.J., and Pazzani, M.J. (2000, January 20\u201323). Scaling up dynamic time warping for datamining applications. Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD\u201900), Boston, MA, USA.","DOI":"10.1145\/347090.347153"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1007\/PL00011669","article-title":"Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases","volume":"3","author":"Keogh","year":"2001","journal-title":"Knowl. Inf. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhang, H., Dong, Y., and Xu, D. (2020, January 11\u201313). Entropy-based Symbolic Aggregate Approximation Representation Method for Time Series. Proceedings of the 2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), Chongqing, China.","DOI":"10.1109\/ITAIC49862.2020.9339021"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.neucom.2014.01.045","article-title":"An improvement of symbolic aggregate approximation distance measure for time series","volume":"138","author":"Sun","year":"2014","journal-title":"Neurocomputing"},{"key":"ref_13","unstructured":"Sch\u00e4fer, P., and H\u00f6gqvist, M. (2020, January 27\u201330). SFA. Proceedings of the 15th International Conference on Extending Database Technology (EDBT\u201912), Berlin, Germany."},{"key":"ref_14","first-page":"1505","article-title":"The BOSS is concerned with time series classification in the presence of noise","volume":"29","year":"2014","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Middlehurst, M., Vickers, W., and Bagnall, A. (2019). Scalable Dictionary Classifiers for Time Series Classification. Intelligent Data Engineering and Automated Learning\u2014IDEAL 2019, Springer International Publishing.","DOI":"10.1007\/978-3-030-33607-3_2"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.3233\/IDA-184333","article-title":"On time series classification with dictionary-based classifiers","volume":"23","author":"Large","year":"2019","journal-title":"Intell. Data Anal."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Sch\u00e4fer, P., and Leser, U. (2017, January 6\u201310). Fast and Accurate Time Series Classification with WEASEL. Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, Singapore.","DOI":"10.1145\/3132847.3132980"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1007\/s10844-012-0196-5","article-title":"Rotation-invariant similarity in time series using bag-of-patterns representation","volume":"39","author":"Lin","year":"2012","journal-title":"J. Intell. Inf. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1007\/s10618-014-0361-2","article-title":"Time series classification with ensembles of elastic distance measures","volume":"29","author":"Lines","year":"2014","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1007\/s10618-019-00617-3","article-title":"Proximity Forest: An effective and scalable distance-based classifier for time series","volume":"33","author":"Lucas","year":"2019","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Xi, X., Keogh, E., Shelton, C., Wei, L., and Ratanamahatana, C.A. (2006, January 25\u201329). Fast time series classification using numerosity reduction. Proceedings of the 23rd International Conference on MACHINE Learning (ICML\u201906), Pittsburgh, PA, USA.","DOI":"10.1145\/1143844.1143974"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.knosys.2014.02.011","article-title":"Non-isometric transforms in time series classification using DTW","volume":"61","author":"uczak","year":"2014","journal-title":"Knowl. Based Syst."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Datta, S., Karmakar, C.K., and Palaniswami, M. (2020, January 1\u20134). Averaging Methods using Dynamic Time Warping for Time Series Classification. Proceedings of the 2020 IEEE Symposium Series on Computational Intelligence (SSCI), Canberra, ACT, Australia.","DOI":"10.1109\/SSCI47803.2020.9308409"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2787","DOI":"10.1016\/j.ins.2011.03.001","article-title":"Dynamic time warping constraint learning for large margin nearest neighbor classification","volume":"181","author":"Yu","year":"2011","journal-title":"Inf. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Forechi, A., Souza, A.F.D., Badue, C., and Oliveira-Santos, T. (2016, January 24\u201329). Sequential appearance-based Global Localization using an ensemble of kNN-DTW classifiers. Proceedings of the 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, Canada.","DOI":"10.1109\/IJCNN.2016.7727550"},{"key":"ref_26","first-page":"2069","article-title":"Reducing statistical time-series problems to binary classification","volume":"3","author":"Ryabko","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.ins.2013.02.030","article-title":"A time series forest for classification and feature extraction","volume":"239","author":"Deng","year":"2013","journal-title":"Inf. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Cabello, N., Naghizade, E., Qi, J., and Kulik, L. (2020, January 17\u201320). Fast and Accurate Time Series Classification Through Supervised Interval Search. Proceedings of the 2020 IEEE International Conference on Data Mining (ICDM), Sorrento, Italy.","DOI":"10.1109\/ICDM50108.2020.00107"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Lines, J., Taylor, S., and Bagnall, A. (2016, January 12\u201315). HIVE-COTE: The Hierarchical Vote Collective of Transformation-Based Ensembles for Time Series Classification. Proceedings of the 2016 IEEE 16th International Conference on Data Mining (ICDM), Barcelona, Spain.","DOI":"10.1109\/ICDM.2016.0133"},{"key":"ref_30","unstructured":"Ye, L., and Keogh, E. (July, January 28). Time series shapelets. Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD\u201909), Paris, France."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1007\/s10618-013-0322-1","article-title":"Classification of time series by shapelet transformation","volume":"28","author":"Hills","year":"2013","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1016\/j.procs.2018.03.025","article-title":"A Shapelet Selection Algorithm for Time Series Classification: New Directions","volume":"129","author":"Ji","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.comnet.2018.11.031","article-title":"A fast shapelet selection algorithm for time series classification","volume":"148","author":"Ji","year":"2019","journal-title":"Comput. Netw."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1454","DOI":"10.1007\/s10618-020-00701-z","article-title":"ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels","volume":"34","author":"Dempster","year":"2020","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Yu, J., Yin, J., Zhou, D., and Zhang, J. (2006). A Pattern Distance-Based Evolutionary Approach to Time Series Segmentation. Intelligent Control and Automation, Springer.","DOI":"10.1007\/978-3-540-37256-1_99"},{"key":"ref_36","unstructured":"Chung, F., Fu, T., Luk, W., and Ng, V. (2001). Flexible time series pattern matching based on perceptually important points. Workshop on Learning from Temporal and Spatial Data in International Joint Conference on Artificial Intelligence, The Hong Kong Polytechnic University."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1389","DOI":"10.3844\/jcssp.2010.1389.1395","article-title":"Index Financial Time Series Based on Zigzag-Perceptually Important Points","volume":"6","author":"Phetchanchai","year":"2010","journal-title":"J. Comput. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Chi, X., and Jiang, Z. (2012, January 29\u201331). Feature recognition of the futures time series based on perceptually important points. Proceedings of the 2012 2nd International Conference on Computer Science and Network Technology, Changchun, China.","DOI":"10.1109\/ICCSNT.2012.6526233"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1318","DOI":"10.1109\/JAS.2019.1911777","article-title":"Self-learning of multivariate time series using perceptually important points","volume":"6","author":"Lintonen","year":"2019","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Fu, T.C., Chung, F.L., and Ng, C.M. (2006, January 26\u201329). Financial Time Series Segmentation based on Specialized Binary Tree Representation. Proceedings of the 2006 International Conference on Data Mining (DMIN 2006), Las Vegas, NV, USA.","DOI":"10.2991\/jcis.2006.30"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Azimifar, M., Araabi, B.N., and Moradi, H. (2020, January 29\u201330). Forecasting stock market trends using support vector regression and perceptually important points. Proceedings of the 2020 10th International Conference on Computer and Knowledge Engineering (ICCKE), Mashhad, Iran.","DOI":"10.1109\/ICCKE50421.2020.9303667"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Fenton, N., and Neil, M. (2018). Decision Analysis, Decision Trees, Value of Information Analysis, and Sensitivity Analysis. Risk Assessment and Decision Analysis with Bayesian Networks, Chapman and Hall\/CRC.","DOI":"10.1201\/b21982"},{"key":"ref_43","first-page":"135","article-title":"A framework for sensitivity analysis of decision trees","volume":"26","author":"Jakubczyk","year":"2017","journal-title":"Cent. Eur. J. Oper. Res."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/S0020-7373(87)80053-6","article-title":"Simplifying decision trees","volume":"27","author":"Quinlan","year":"1987","journal-title":"Int. J. Man-Mach. Stud."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Kretowski, M. (2019). Decision Trees in Data Mining. Studies in Big Data, Springer International Publishing.","DOI":"10.1007\/978-3-030-21851-5_2"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"7680","DOI":"10.1016\/j.eswa.2012.01.051","article-title":"Forecasting shanghai composite index based on fuzzy time series and improved C-fuzzy decision trees","volume":"39","author":"Qiu","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.knosys.2016.08.028","article-title":"Exploring shapelet transformation for time series classification in decision trees","volume":"112","author":"Zalewski","year":"2016","journal-title":"Knowl. Based Syst."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"He, Y., Chu, X., and Wang, Y. (2020, January 20\u201324). Neighbor Profile: Bagging Nearest Neighbors for Unsupervised Time Series Mining. Proceedings of the 2020 IEEE 36th International Conference on Data Engineering (ICDE), Dallas, TX, USA.","DOI":"10.1109\/ICDE48307.2020.00039"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1007\/s11749-016-0488-0","article-title":"Rejoinder on: A random forest guided tour","volume":"25","author":"Biau","year":"2016","journal-title":"Test"},{"key":"ref_50","first-page":"1612","article-title":"A short introduction to boosting","volume":"14","author":"Freund","year":"1999","journal-title":"J. Jpn. Soc. Artif. Intell."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"03024","DOI":"10.1051\/matecconf\/202030903024","article-title":"Time series classification based on arima and adaboost","volume":"309","author":"Wang","year":"2020","journal-title":"MATEC Web Conf."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/S0167-9473(01)00065-2","article-title":"Stochastic gradient boosting","volume":"38","author":"Friedman","year":"2002","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1108\/IJWIS-05-2018-0041","article-title":"Enhanced prediction of vulnerable Web components using Stochastic Gradient Boosting Trees","volume":"15","author":"Elish","year":"2019","journal-title":"Int. J. Web Inf. Syst."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.atmosenv.2018.04.019","article-title":"Using a gradient boosting model to improve the performance of low-cost aerosol monitors in a dense, heterogeneous urban environment","volume":"184","author":"Johnson","year":"2018","journal-title":"Atmos. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1023\/B:MACH.0000015881.36452.6e","article-title":"Is Combining Classifiers with Stacking Better than Selecting the Best One?","volume":"54","year":"2004","journal-title":"Mach. Learn."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Fuad, M.M.M. (2020). Extreme-SAX: Extreme Points Based Symbolic Representation for Time Series Classification. Big Data Analytics and Knowledge Discovery, Springer International Publishing.","DOI":"10.1007\/978-3-030-59065-9_10"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Yan, L., Liu, Y., and Liu, Y. (2020). Interval Feature Transformation for Time Series Classification Using Perceptually Important Points. Appl. Sci., 10.","DOI":"10.3390\/app10165428"},{"key":"ref_58","unstructured":"Dorle, A., Li, F., Song, W., and Li, S. (2018, January 19\u201323). Learning Discriminative Virtual Sequences for Time Series Classification. Proceedings of the 29th ACM International Conference on Information & Knowledge Management, Online."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/23\/8\/1059\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:45:16Z","timestamp":1760165116000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/23\/8\/1059"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,17]]},"references-count":58,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["e23081059"],"URL":"https:\/\/doi.org\/10.3390\/e23081059","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,17]]}}}