Time series classification with random temporal features

Abanda, 2019, A review on distance based time series classification, Data Min. Knowl. Disc., 33, 378, 10.1007/s10618-018-0596-4 Amouri, 2023, Constrained dtw preserving shapelets for explainable time-series clustering, Pattern Recogn., 109804 Baghizadeh, 2020, A new emotion detection algorithm using extracted features of the different time-series generated from st intervals poincaré map, Biomed. Signal Process. Control, 59, 101902, 10.1016/j.bspc.2020.101902 Bagnall, 2017, The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances, Data Min. Knowl. Disc., 31, 606, 10.1007/s10618-016-0483-9 Baldán, 2023, Complexity measures and features for times series classification, Expert Syst. Appl., 213, 119227, 10.1016/j.eswa.2022.119227 Chung, 2004, An evolutionary approach to pattern-based time series segmentation, IEEE Trans. Evol. Comput., 8, 471, 10.1109/TEVC.2004.832863 Dau, H.A., Keogh, E., Kamgar, K., Yeh, C.-C.M., Zhu, Y., Gharghabi, S., Ratanamahatana, C.A., Yanping, Hu, N., Bing nd Begum, Bagnall, A., Mueen, A., Batista, G., 2018. Hexagon-ML, The ucr time series classification archive, https://www.cs.ucr.edu/eamonn/time_series_data_2018/ (October 2018). Dempster, 2020, Rocket: exceptionally fast and accurate time series classification using random convolutional kernels, Data Min. Knowl. Disc., 34, 1454, 10.1007/s10618-020-00701-z Dempster, A., Schmidt, D.F., Webb, G.I., 2021. Minirocket: A very fast (almost) deterministic transform for time series classification. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 248–257. Dempster, 2023, Hydra: Competing convolutional kernels for fast and accurate time series classification, Data Min. Knowl. Disc., 1 Deng, 2013, A time series forest for classification and feature extraction, Inf. Sci., 239, 142, 10.1016/j.ins.2013.02.030 Du, 2023, Multi-feature based network for multivariate time series classification, Inf. Sci., 639, 119009, 10.1016/j.ins.2023.119009 Esling, 2012, Time-series data mining, ACM Comput. Surv., 45, 1, 10.1145/2379776.2379788 Fang, 2018, Efficient learning interpretable shapelets for accurate time series classification, 497 Fawaz, 2019, Deep learning for time series classification: a review, Data Min. Knowl. Disc., 33, 917, 10.1007/s10618-019-00619-1 Foumani, N.M., Miller, L., Tan, C.W., Webb, G.I., Forestier, G., Salehi, M., 2023. Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey. arXiv:2302.02515. Gordon, 2015, Fast and space-efficient shapelets-based time-series classification, Intell. Data Anal., 19, 953, 10.3233/IDA-150753 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. ACM, pp. 392–401. Hao, 2023, MICOS: Mixed supervised contrastive learning for multivariate time series classification, Knowl.-Based Syst., 260, 110158, 10.1016/j.knosys.2022.110158 Hou, L., Kwok, J., Zurada, J., 2016. Efficient learning of timeseries shapelets. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 30. Hsu, 2021, Multiple time-series convolutional neural network for fault detection and diagnosis and empirical study in semiconductor manufacturing, J. Intell. Manuf., 32, 823, 10.1007/s10845-020-01591-0 Ji, 2019, A fast shapelet selection algorithm for time series classification, Comput. Networks, 148, 231, 10.1016/j.comnet.2018.11.031 Ji, 2019, Xg-sf: An xgboost classifier based on shapelet features for time series classification, Proc. Comput. Sci., 147, 24, 10.1016/j.procs.2019.01.179 Ji, 2020, Adarc: An anomaly detection algorithm based on relative outlier distance and biseries correlation, Softw. Practice Exp., 50, 2065, 10.1002/spe.2756 Ji, 2022, Fully convolutional networks with shapelet features for time series classification, Inf. Sci., 612, 835, 10.1016/j.ins.2022.09.009 Ji, 2022, Time series classification based on temporal features, Appl. Soft Comput., 128, 109494, 10.1016/j.asoc.2022.109494 Karlsson, 2016, Generalized random shapelet forests, Data Min. Knowl. Disc., 30, 1053, 10.1007/s10618-016-0473-y Kate, 2016, Using dynamic time warping distances as features for improved time series classification, Data Min. Knowl. Disc., 30, 283, 10.1007/s10618-015-0418-x Le Nguyen, 2019, Interpretable time series classification using linear models and multi-resolution multi-domain symbolic representations, Data Mini. Knowledge Discov., 33, 1183, 10.1007/s10618-019-00633-3 Li, 2020, Efficient shapelet discovery for time series classification, IEEE Trans. Knowledge Data Eng., 34, 1149, 10.1109/TKDE.2020.2995870 Li, 2023, A two-phase filtering of discriminative shapelets learning for time series classification, Appl. Intell., 53, 13815, 10.1007/s10489-022-04043-9 Lines, 2012, Alternative quality measures for time series shapelets, 475 Lines, J., Davis, L.M., Hills, J., Bagnall, A., 2012. A shapelet transform for time series classification. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, pp. 289–297. Liu, 2023, Time series classification based on convolutional network with a Gated Linear Units kernel, Eng. Appl. Artif. Intell., 123, 106296, 10.1016/j.engappai.2023.106296 Lubba, 2019, catch22: Canonical time-series characteristics, Data Min. Knowl. Disc., 33, 1821, 10.1007/s10618-019-00647-x Lucas, 2019, Proximity forest: an effective and scalable distance-based classifier for time series, Data Min. Knowl. Disc., 33, 607, 10.1007/s10618-019-00617-3 Lu, 2021, Detection of abnormal brain in mri via improved alexnet and elm optimized by chaotic bat algorithm, Neural Comput. Appl., 33, 10799, 10.1007/s00521-020-05082-4 Lu, 2022, Nagnn: classification of covid-19 based on neighboring aware representation from deep graph neural network, Int. J. Intell. Syst., 37, 1572, 10.1002/int.22686 Ma, 2019, 1246 Middlehurst, 2019, Scalable dictionary classifiers for time series classification, 11 Middlehurst, M., Schäfer, P., Bagnall, A., 2023. Bake off redux: a review and experimental evaluation of recent time series classification algorithms. arXiv:2304.13029. Nanopoulos, 2001, Feature-based classification of time-series data, Int. J. Comput. Res., 10, 49 Nembrini, 2018, The revival of the gini importance?, Bioinformatics, 34, 3711, 10.1093/bioinformatics/bty373 Prieto, 2015, Stacking for multivariate time series classification, Pattern. Anal. Appl., 18, 297, 10.1007/s10044-013-0351-9 Rakthanmanon, T., Keogh, E., 2013. Fast shapelets: A scalable algorithm for discovering time series shapelets. In: proceedings of the 2013 SIAM International Conference on Data Mining. SIAM, pp. 668–676. Renard, 2015, Random-shapelet: an algorithm for fast shapelet discovery, 1 Ruiz, 2020, The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances, Data Min. Knowl. Disc., 1 Shi, 2018, Random pairwise shapelets forest, 68 Sun, 2023, Few-shot class-incremental learning for medical time series classification, IEEE J. Biomed. Health Informat., 1 Wang, 2019, Time series feature learning with labeled and unlabeled data, Pattern Recogn., 89, 55, 10.1016/j.patcog.2018.12.026 Wei, Y., Wang, Y., Du, M., Hu, Y., Ji, C. 2023. Adaptive shapelet selection for time series classification. In: 2023 26th International Conference on Computer Supported Cooperative Work in Design. IEEE, pp. 1607–1612. Wilcoxon, 1992 Wu, 2021, Pfc: A novel perceptual features-based framework for time series classification, Entropy, 23, 1059, 10.3390/e23081059 Xiao, 2021, Rtfn: A robust temporal feature network for time series classification, Inf. Sci., 571, 65, 10.1016/j.ins.2021.04.053 Yan, 2020, Application of discrete wavelet transform in shapelet-based classification, Mathe. Probl. Eng., 10.1155/2020/6523872 Yang, 2023, Attentional gated Res2Net for multivariate time series classification, Neural Process. Lett., 55, 1371, 10.1007/s11063-022-10944-0 Yang, 2023, Accurate and fast time series classification based on compressed random Shapelet Forest, Appl. Intell., 53, 5240 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. ACM, pp. 947–956. Ye, 2011, Time series shapelets: a novel technique that allows accurate, interpretable and fast classification, Data Min. Knowledge Disc., 22, 149, 10.1007/s10618-010-0179-5 Yuan, 2022, Random pairwise shapelets forest: an effective classifier for time series, Knowl. Inf. Syst., 64, 143, 10.1007/s10115-021-01630-z Zhang, 2005, Blind feature extraction for time-series classification using haar wavelet transform, 605 Zhang, 2023, Deep Learning in Food Category Recognition, Infor. Fus., 101859, 10.1016/j.inffus.2023.101859