{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T01:21:24Z","timestamp":1790385684661,"version":"4.1.0"},"reference-count":34,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2019,12,23]],"date-time":"2019-12-23T00:00:00Z","timestamp":1577059200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Minister of Science and Higher Education of the Republic of Poland","award":["027\/RID\/2018\/19"],"award-info":[{"award-number":["027\/RID\/2018\/19"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper, a novel data augmentation method for time-series classification is proposed. In the introduced method, a new time-series is obtained in warped space between suboptimally aligned input examples of different lengths. Specifically, the alignment is carried out constraining the warping path and reducing its flexibility. It is shown that the resultant synthetic time-series can form new class boundaries and enrich the training dataset. In this work, the comparative evaluation of the proposed augmentation method against related techniques on representative multivariate time-series datasets is presented. The performance of methods is examined using the nearest neighbor classifier with the dynamic time warping (NN-DTW), LogDet divergence-based metric learning with triplet constraints (LDMLT), and the recently introduced time-series cluster kernel (NN-TCK). The impact of the augmentation on the classification performance is investigated, taking into account entire datasets and cases with a small number of training examples. The extensive evaluation reveals that the introduced method outperforms related augmentation algorithms in terms of the obtained classification accuracy.<\/jats:p>","DOI":"10.3390\/s20010098","type":"journal-article","created":{"date-parts":[[2019,12,23]],"date-time":"2019-12-23T04:23:01Z","timestamp":1577074981000},"page":"98","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":66,"title":["Data Augmentation with Suboptimal Warping for Time-Series Classification"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7922-7560","authenticated-orcid":false,"given":"Krzysztof","family":"Kamycki","sequence":"first","affiliation":[{"name":"Department of Computer and Control Engineering, Rzeszow University of Technology, W. Pola 2, 35-959 Rzeszow, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4084-8113","authenticated-orcid":false,"given":"Tomasz","family":"Kapuscinski","sequence":"additional","affiliation":[{"name":"Department of Computer and Control Engineering, Rzeszow University of Technology, W. Pola 2, 35-959 Rzeszow, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5482-6313","authenticated-orcid":false,"given":"Mariusz","family":"Oszust","sequence":"additional","affiliation":[{"name":"Department of Computer and Control Engineering, Rzeszow University of Technology, W. Pola 2, 35-959 Rzeszow, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,23]]},"reference":[{"key":"ref_1","first-page":"012003","article-title":"Multivariate time series classification analysis: State-of-the-art and future challenges","volume":"536","author":"Handhika","year":"2019","journal-title":"J. Phys. Conf. Ser. Mater. Sci. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"36","DOI":"10.5772\/60091","article-title":"Recognition of hand gestures observed by depth cameras","volume":"12","author":"Kapuscinski","year":"2015","journal-title":"Int. J. Adv. Rob. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhang, H.B., Zhang, Y.X., Zhong, B., Lei, Q., Yang, L., Du, J.X., and Chen, D.S. (2019). A comprehensive survey of vision-based human action recognition methods. Sensors, 19.","DOI":"10.3390\/s19051005"},{"key":"ref_4","unstructured":"Le Guennec, A., Malinowski, S., and Tavenard, R. (, January September). Data Augmentation for Time Series Classification using Convolutional Neural Networks. Proceedings of the AALTD 2016: Second ECML\/PKDD International Workshop on Advanced Analytics and Learning on Temporal Data, Riva Del Garda, Italy."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_6","unstructured":"Fawaz, H.I., Forestier, G., Weber, J., Idoumghar, L., and Muller, P.A. (2019, January 14\u201319). Adversarial Attacks on Deep Neural Networks for Time Series Classification. Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Um, T.T., Pfister, F.M.J., Pichler, D., Endo, S., Lang, M., Hirche, S., Fietzek, U., and Kuli\u0107, D. (2017, January 13\u201317). Data Augmentation of Wearable Sensor Data for Parkinson\u2019s Disease Monitoring using Convolutional Neural Networks. Proceedings of the ACM International Conference on Multimodal Interaction, Glasgow, UK.","DOI":"10.1145\/3136755.3136817"},{"key":"ref_8","unstructured":"Fawaz, H.I., Forestier, G., Weber, J., Idoumghar, L., and Muller, P.A. (2018). Data augmentation using synthetic data for time series classification with deep residual networks. arXiv."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Forestier, G., Petitjean, F., Dau, H.A., Webb, G.I., and Keogh, E. (2017, January 18\u201321). Generating Synthetic Time Series to Augment Sparse Datasets. Proceedings of the 2017 IEEE International Conference on Data Mining (ICDM), New Orleans, LA, USA.","DOI":"10.1109\/ICDM.2017.106"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Haradal, S., Hayashi, H., and Uchida, S. (2018, January 17\u201321). Biosignal Data Augmentation Based on Generative Adversarial Networks. Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA.","DOI":"10.1109\/EMBC.2018.8512396"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/TASSP.1978.1163055","article-title":"Dynamic programming algorithm optimization for spoken word recognition","volume":"26","author":"Sakoe","year":"1978","journal-title":"IEEE Trans. Acoust. Speech"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.ins.2019.06.007","article-title":"Geometric SMOTE a geometrically enhanced drop-in replacement for SMOTE","volume":"501","author":"Douzas","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_13","unstructured":"Ramponi, G., Protopapas, P., Brambilla, M., and Janssen, R. (2018). T-CGAN: Conditional Generative Adversarial Network for Data Augmentation in Noisy Time Series with Irregular Sampling. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"101675","DOI":"10.1016\/j.bspc.2019.101675","article-title":"A novel data augmentation method to enhance deep neural networks for detection of atrial fibrillation","volume":"56","author":"Cao","year":"2020","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_15","unstructured":"Delaney, A.M., Brophy, E., and Ward, T.E. (2019). Synthesis of Realistic ECG using Generative Adversarial Networks. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"131248","DOI":"10.1109\/ACCESS.2019.2940701","article-title":"Simulating Time-Series Data for Improved Deep Neural Network Performance","volume":"7","author":"Yeomans","year":"2019","journal-title":"IEEE Access"},{"key":"ref_17","unstructured":"Krell, M.M., Seeland, A., and Kim, S.K. (2018). Data Augmentation for Brain-Computer Interfaces: Analysis on Event-Related Potentials Data. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Dahmen, J., and Cook, D. (2019). SynSys: A Synthetic Data Generation System for Healthcare Applications. Sensors, 19.","DOI":"10.3390\/s19051181"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ruiz, V., Linares, I., Sanchez, A., and Velez, J.F. (2019). Off-line handwritten signature verification using compositional synthetic generation of signatures and Siamese Neural Networks. Neurocomputing.","DOI":"10.1016\/j.neucom.2019.09.041"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Devaney, J., Mandel, M.I., and Ellis, D.P.W. (2009, January 18\u201321). Improving MIDI-audio alignment with acoustic features. Proceedings of the 2009 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, New Paltz, NY, USA.","DOI":"10.1109\/ASPAA.2009.5346500"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.isprsjprs.2019.03.002","article-title":"Robust registration for remote sensing images by combining and localizing feature- and area-based methods","volume":"151","author":"Feng","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_22","unstructured":"Kornfield, E.M., Manmatha, R., and Allan, J. (2004, January 23\u201324). Text alignment with handwritten documents. Proceedings of the First International Workshop on Document Image Analysis for Libraries, Palo Alto, CA, USA."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Tan, C.W., Herrmann, M., Forestier, G., Webb, G.I., and Petitjean, F. (2018, January 3\u20135). Efficient search of the best warping window for Dynamic Time Warping. Proceedings of the 2018 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, San Diego, CA, USA.","DOI":"10.1137\/1.9781611975321.26"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1016\/j.patcog.2017.11.030","article-title":"Time series cluster kernel for learning similarities between multivariate time series with missing data","volume":"76","author":"Mikalsen","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_25","unstructured":"Olszewski, R.T. (2019, December 22). ECG Database. Available online: http:\/\/www.cs.cmu.edu\/~bobski\/data\/data.html."},{"key":"ref_26","unstructured":"Dua, D., and Graff, C. (2019, December 22). UCI Machine Learning Repository. Available online: http:\/\/archive.ics.uci.edu\/ml."},{"key":"ref_27","unstructured":"CMU Graphics Lab (2019, December 22). CMU Graphics Lab Motion Capture Database Converted to FBX. Available online: http:\/\/mocap.cs.cmu.edu\/."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1363","DOI":"10.1109\/TCYB.2015.2426723","article-title":"Learning a Mahalanobis Distance-Based Dynamic Time Warping Measure for Multivariate Time Series Classification","volume":"46","author":"Mei","year":"2016","journal-title":"IEEE Trans. Cybern."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"67718","DOI":"10.1109\/ACCESS.2019.2916828","article-title":"Insights Into LSTM Fully Convolutional Networks for Time Series Classification","volume":"7","author":"Karim","year":"2019","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.eswa.2015.08.055","article-title":"An effective multivariate time series classification approach using echo state network and adaptive differential evolution algorithm","volume":"43","author":"Wang","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/BF02289565","article-title":"Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis","volume":"29","author":"Kruskal","year":"1964","journal-title":"Psychometrika"},{"key":"ref_32","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":"Forestier","year":"2019","journal-title":"Data Mining Knowledge Discovery"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Kapuscinski, T., and Organisciak, P. (2018). Handshape Recognition Using Skeletal Data. Sensors, 18.","DOI":"10.3390\/s18082577"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Warchol, D., Kapuscinski, T., and Wysocki, M. (2019). Recognition of Fingerspelling Sequences in Polish Sign Language Using Point Clouds Obtained from Depth Images. Sensors, 19.","DOI":"10.3390\/s19051078"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/1\/98\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:44:51Z","timestamp":1760190291000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/1\/98"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,23]]},"references-count":34,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2020,1]]}},"alternative-id":["s20010098"],"URL":"https:\/\/doi.org\/10.3390\/s20010098","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,23]]}}}