{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T20:23:32Z","timestamp":1783023812272,"version":"3.54.6"},"publisher-location":"Singapore","reference-count":28,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819219254","type":"print"},{"value":"9789819219261","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:00:00Z","timestamp":1782777600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:00:00Z","timestamp":1782777600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2027]]},"DOI":"10.1007\/978-981-92-1926-1_6","type":"book-chapter","created":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T15:48:38Z","timestamp":1782748118000},"page":"65-77","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Self-supervised Learning for\u00a0Time Series Classification via\u00a0Redundancy Reduction and\u00a0Wavelet-Based Data Augmentation"],"prefix":"10.1007","author":[{"given":"Daravichet","family":"Tin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hassan","family":"Habibi Gharakheili","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gustavo","family":"Batista","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,30]]},"reference":[{"issue":"6","key":"6_CR1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.64.061907","volume":"64","author":"RG Andrzejak","year":"2001","unstructured":"Andrzejak, R.G., Lehnertz, K., Mormann, F., Rieke, C., David, P., Elger, C.E.: Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. Phys. Rev. E 64(6), 061907 (2001)","journal-title":"Phys. Rev. E"},{"issue":"12","key":"6_CR2","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1109\/MSPEC.1967.5217220","volume":"4","author":"EO Brigham","year":"1967","unstructured":"Brigham, E.O., Morrow, R.E.: The fast fourier transform. IEEE Spectr. 4(12), 63\u201370 (1967). https:\/\/doi.org\/10.1109\/MSPEC.1967.5217220","journal-title":"IEEE Spectr."},{"key":"6_CR3","unstructured":"Burrus, C.S.: Introduction to wavelets and wavelet transforms: a primer. Englewood Cliffs (1997)"},{"issue":"2","key":"6_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1883612.1883613","volume":"43","author":"P Chaovalit","year":"2011","unstructured":"Chaovalit, P., Gangopadhyay, A., Karabatis, G., Chen, Z.: Discrete wavelet transform-based time series analysis and mining. ACM CSUR 43(2), 1\u201337 (2011)","journal-title":"ACM CSUR"},{"key":"6_CR5","unstructured":"Cuturi, M., Blondel, M.: Soft-DTW: a differentiable loss function for time-series. In: ICML, pp. 894\u2013903. PMLR (2017)"},{"key":"6_CR6","unstructured":"Dempster, A., et al.: Monster: monash scalable time series evaluation repository (2025). arXiv preprint arXiv:2502.15122"},{"key":"6_CR7","doi-asserted-by":"crossref","unstructured":"Eldele, E., et al.: Time-series representation learning via temporal and contextual contrasting. In: IJCAI-21, pp. 2352\u20132359 (2021)","DOI":"10.24963\/ijcai.2021\/324"},{"issue":"3","key":"6_CR8","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1109\/MSP.2021.3134634","volume":"39","author":"L Ericsson","year":"2022","unstructured":"Ericsson, L., Gouk, H., Loy, C.C., Hospedales, T.M.: Self-supervised representation learning: introduction, advances, and challenges. IEEE Signal Process. Mag. 39(3), 42\u201362 (2022)","journal-title":"IEEE Signal Process. Mag."},{"issue":"1","key":"6_CR9","first-page":"1","volume":"45","author":"P Esling","year":"2012","unstructured":"Esling, P., Agon, C.: Time-series data mining. ACM CSUR 45(1), 1\u201334 (2012)","journal-title":"Time-series data mining. ACM CSUR"},{"issue":"4","key":"6_CR10","doi-asserted-by":"publisher","first-page":"2520","DOI":"10.1007\/s10618-024-01043-w","volume":"38","author":"NM Foumani","year":"2024","unstructured":"Foumani, N.M., Tan, C.W., Webb, G.I., Rezatofighi, H., Salehi, M.: Series2vec: similarity-based self-supervised representation learning for time series classification. Data Min. Knowl. Disc. 38(4), 2520\u20132544 (2024)","journal-title":"Data Min. Knowl. Disc."},{"key":"6_CR11","unstructured":"Franceschi, J.Y., Dieuleveut, A., Jaggi, M.: Unsupervised scalable representation learning for multivariate time series. NeurIPS 32 (2019)"},{"key":"6_CR12","doi-asserted-by":"crossref","unstructured":"Galib, A.H., Tan, P.N., Luo, L.: Simext: self-supervised representation learning for extreme values in time series. In: IEEE ICDM, pp. 1031\u20131036. IEEE (2023)","DOI":"10.1109\/ICDM58522.2023.00119"},{"issue":"23","key":"6_CR13","doi-asserted-by":"publisher","first-page":"e215","DOI":"10.1161\/01.CIR.101.23.e215","volume":"101","author":"AL Goldberger","year":"2000","unstructured":"Goldberger, A.L., et al.: Physiobank, Physiotoolkit, and Physionet: components of a new research resource for complex physiologic signals. Circulation 101(23), e215\u2013e220 (2000)","journal-title":"Circulation"},{"key":"6_CR14","volume-title":"Large scale time-series representation learning via simultaneous low-and high-frequency feature bootstrapping","author":"V Gorade","year":"2023","unstructured":"Gorade, V., Singh, A., Mishra, D.: Large scale time-series representation learning via simultaneous low-and high-frequency feature bootstrapping. IEEE Trans. Neural Netw. Learn, Syst (2023)"},{"key":"6_CR15","unstructured":"Grill, J.B., et al.: Bootstrap your own latent-a new approach to self-supervised learning. NeurIPS 33 (2020)"},{"issue":"4","key":"6_CR16","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., Idoumghar, L., Muller, P.-A.: Deep learning for time series classification: a review. Data Min. Knowl. Disc. 33(4), 917\u2013963 (2019). https:\/\/doi.org\/10.1007\/s10618-019-00619-1","journal-title":"Data Min. Knowl. Disc."},{"key":"6_CR17","doi-asserted-by":"crossref","unstructured":"Li, D., Bissyande, T.F.D.A., Klein, J., Le Traon, Y.: Time series classification with discrete wavelet transformed data: insights from an empirical study. In: SEKE 2016 (2016)","DOI":"10.18293\/SEKE2016-067"},{"key":"6_CR18","doi-asserted-by":"crossref","unstructured":"Liu, J., Chen, S.: Timesurl: self-supervised contrastive learning for universal time series representation learning. In: AAAI. vol. 38, pp. 13918\u201313926 (2024)","DOI":"10.1609\/aaai.v38i12.29299"},{"issue":"1","key":"6_CR19","first-page":"857","volume":"35","author":"X Liu","year":"2021","unstructured":"Liu, X., et al.: Self-supervised learning: generative or contrastive. IEEE TKDE 35(1), 857\u2013876 (2021)","journal-title":"IEEE TKDE"},{"key":"6_CR20","unstructured":"Liu, Z., Alavi, A., Li, M., Zhang, X.: Self-supervised learning for time series. In: Contrastive or Generative? (2024). arXiv:2403.09809"},{"key":"6_CR21","doi-asserted-by":"crossref","unstructured":"Luo, D., et al.: Time series contrastive learning with information-aware augmentations. In: AAAI. vol. 37, pp. 4534\u20134542 (2023)","DOI":"10.1609\/aaai.v37i4.25575"},{"key":"6_CR22","doi-asserted-by":"crossref","unstructured":"Nguyen, D.A., Tran, T.H., Pham, H.H., Le Nguyen, P., Nguyen, L.M.: Improving time series encoding with noise-aware self-supervised learning and an efficient encoder. In: IEEE ICDM, pp. 340\u2013349. IEEE (2024)","DOI":"10.1109\/ICDM59182.2024.00041"},{"key":"6_CR23","unstructured":"Robinson, J., Chuang, C.Y., Sra, S., Jegelka, S.: Contrastive learning with hard negative samples (2020). arXiv preprint arXiv:2010.04592"},{"key":"6_CR24","unstructured":"Tonekaboni, S., Eytan, D., Goldenberg, A.: Unsupervised representation learning for time series with temporal neighborhood coding. In: ICLR (2021)"},{"key":"6_CR25","doi-asserted-by":"crossref","unstructured":"Wang, Z., Yan, W., Oates, T.: Time series classification from scratch with deep neural networks: a strong baseline. In: IJCNN, pp. 1578\u20131585. IEEE (2017)","DOI":"10.1109\/IJCNN.2017.7966039"},{"key":"6_CR26","doi-asserted-by":"crossref","unstructured":"Yue, Z., et al.: Ts2vec: towards universal representation of time series. In: AAAI. vol 36 (2022)","DOI":"10.1609\/aaai.v36i8.20881"},{"key":"6_CR27","unstructured":"Zbontar, J., Jing, L., Misra, I., LeCun, Y., Deny, S.: Barlow twins: self-supervised learning via redundancy reduction. In: ICML, pp. 12310\u201312320. PMLR (2021)"},{"key":"6_CR28","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhao, Z., Tsiligkaridis, T., Zitnik, M.: Self-supervised contrastive pre-training for time series via time-frequency consistency. NeurIPS 35 (2022)","DOI":"10.52202\/068431-0288"}],"container-title":["Lecture Notes in Computer Science","Data Science: Foundations and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-1926-1_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T19:45:25Z","timestamp":1783021525000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-1926-1_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,30]]},"ISBN":["9789819219254","9789819219261"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-1926-1_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,30]]},"assertion":[{"value":"30 June 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PAKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hong Kong","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 June 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 June 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pakdd2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.pakdd2026.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}