{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T23:09:02Z","timestamp":1778368142944,"version":"3.51.4"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032191014","type":"print"},{"value":"9783032191021","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-19102-1_10","type":"book-chapter","created":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T22:16:17Z","timestamp":1778364977000},"page":"160-176","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["cPB: Continuous Piggyback for\u00a0Streaming Continual Learning with\u00a0Temporal Dependence"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2692-7547","authenticated-orcid":false,"given":"Reza","family":"Paki","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4210-6271","authenticated-orcid":false,"given":"Federico","family":"Giannini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5176-5885","authenticated-orcid":false,"given":"Emanuele Della","family":"Valle","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,5,10]]},"reference":[{"key":"10_CR1","doi-asserted-by":"crossref","unstructured":"Bifet, A., de\u00a0Francisci\u00a0Morales, G., Read, J., Holmes, G., Pfahringer, B.: Efficient online evaluation of big data stream classifiers. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 59\u201368 (2015)","DOI":"10.1145\/2783258.2783372"},{"key":"10_CR2","doi-asserted-by":"crossref","unstructured":"Bifet, A., Gavald\u00e0, R., Holmes, G., Pfahringer, B.: Machine learning for data streams: with practical examples in MOA. MIT press (2018)","DOI":"10.7551\/mitpress\/10654.001.0001"},{"key":"10_CR3","doi-asserted-by":"crossref","unstructured":"Cossu, A., et al.: Don\u2019t drift away: advances and applications of streaming and continual learning. In: ESANN (2025)","DOI":"10.14428\/esann\/2025.ES2025-23"},{"issue":"7","key":"10_CR4","first-page":"3366","volume":"44","author":"M De Lange","year":"2021","unstructured":"De Lange, M., et al.: A continual learning survey: defying forgetting in classification tasks. IEEE Trans. Pattern Anal. Mach. Intell. 44(7), 3366\u20133385 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"2","key":"10_CR5","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1207\/s15516709cog1402_1","volume":"14","author":"JL Elman","year":"1990","unstructured":"Elman, J.L.: Finding structure in time. Cogn. Sci. 14(2), 179\u2013211 (1990)","journal-title":"Cogn. Sci."},{"key":"10_CR6","doi-asserted-by":"crossref","unstructured":"Gama, J., Medas, P., Castillo, G., Rodrigues, P.P.: Learning with Drift Detection. In: SBIA. LNCS, vol.\u00a03171, pp. 286\u2013295. Springer (2004)","DOI":"10.1007\/978-3-540-28645-5_29"},{"key":"10_CR7","doi-asserted-by":"crossref","unstructured":"Gama, J., Sebasti\u00e3o, R., Rodrigues, P.P.: Issues in evaluation of stream learning algorithms. In: KDD, pp. 329\u2013338. ACM (2009)","DOI":"10.1145\/1557019.1557060"},{"issue":"6","key":"10_CR8","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1109\/MIS.2024.3479469","volume":"39","author":"F Giannini","year":"2024","unstructured":"Giannini, F., Ziffer, G., Cossu, A., Lomonaco, V.: Streaming continual learning for unified adaptive intelligence in dynamic environments. IEEE Intell. Syst. 39(6), 81\u201385 (2024)","journal-title":"IEEE Intell. Syst."},{"issue":"3","key":"10_CR9","doi-asserted-by":"publisher","first-page":"1207","DOI":"10.5194\/essd-10-1207-2018","volume":"10","author":"SE Godsey","year":"2018","unstructured":"Godsey, S.E., et al.: Eleven years of mountain weather, snow, soil moisture and streamflow data from the rain-snow transition zone-the Johnston Draw catchment, Reynolds Creek Experimental Watershed and Critical Zone Observatory, USA. Earth Syst. Sci. Data 10(3), 1207\u20131216 (2018)","journal-title":"Earth Syst. Sci. Data"},{"key":"10_CR10","doi-asserted-by":"publisher","first-page":"1469","DOI":"10.1007\/s10994-017-5642-8","volume":"106","author":"HM Gomes","year":"2017","unstructured":"Gomes, H.M., et al.: Adaptive random forests for evolving data stream classification. Mach. Learn. 106, 1469\u20131495 (2017)","journal-title":"Mach. Learn."},{"key":"10_CR11","unstructured":"Goodfellow, I.J., Bengio, Y., Courville, A.C.: Deep Learning. Adaptive computation and machine learning. MIT Press (2016)"},{"key":"10_CR12","doi-asserted-by":"publisher","unstructured":"Gunasekara, N., Pfahringer, B., Gomes, H.M., Bifet, A.: Survey on online streaming continual learning. In: Thirty-Second International Joint Conference on Artificial Intelligence, vol.\u00a06, pp. 6628\u20136637 (2023). https:\/\/doi.org\/10.24963\/ijcai.2023\/743","DOI":"10.24963\/ijcai.2023\/743"},{"key":"10_CR13","doi-asserted-by":"crossref","unstructured":"Lemos\u00a0Neto, \u00c1.C., Coelho, R.A., Castro, C.L.d.: An incremental learning approach using long short-term memory neural networks. J. Control, Automation Electr. Syst., 1\u20139 (2022)","DOI":"10.1007\/s40313-021-00882-y"},{"key":"10_CR14","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.inffus.2019.12.004","volume":"58","author":"T Lesort","year":"2020","unstructured":"Lesort, T., et al.: Continual learning for robotics: definition, framework, learning strategies, opportunities and challenges. Inf. Fusion 58, 52\u201368 (2020)","journal-title":"Inf. Fusion"},{"issue":"12","key":"10_CR15","first-page":"2346","volume":"31","author":"J Lu","year":"2019","unstructured":"Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., Zhang, G.: Learning under concept drift: a review. IEEE Trans. Knowl. Data Eng. 31(12), 2346\u20132363 (2019)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"4","key":"10_CR16","doi-asserted-by":"publisher","first-page":"1346","DOI":"10.1016\/j.ijforecast.2021.11.013","volume":"38","author":"S Makridakis","year":"2022","unstructured":"Makridakis, S., Spiliotis, E., Assimakopoulos, V.: M5 accuracy competition: Results, findings, and conclusions. Int. J. Forecast. 38(4), 1346\u20131364 (2022)","journal-title":"Int. J. Forecast."},{"key":"10_CR17","doi-asserted-by":"crossref","unstructured":"Mallya, A., Davis, D., Lazebnik, S.: Piggyback: adapting a single network to multiple tasks by learning to mask weights. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 67\u201382 (2018)","DOI":"10.1007\/978-3-030-01225-0_5"},{"key":"10_CR18","doi-asserted-by":"crossref","unstructured":"Parisi, G.I., Lomonaco, V.: Online continual learning on sequences. In: INNSBDDL (Tutorials). Studies in Computational Intelligence, vol.\u00a0896, pp. 197\u2013221. Springer (2019)","DOI":"10.1007\/978-3-030-43883-8_8"},{"key":"10_CR19","doi-asserted-by":"crossref","unstructured":"Read, J., Rios, R.A., Nogueira, T., de\u00a0Mello, R.F.: Data streams are time series: challenging assumptions. In: BRACIS (2). Lecture Notes in Computer Science, vol. 12320, pp. 529\u2013543. Springer (2020)","DOI":"10.1007\/978-3-030-61380-8_36"},{"key":"10_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109604","volume":"141","author":"M Shabani","year":"2023","unstructured":"Shabani, M., Tran, D.T., Kanniainen, J., Iosifidis, A.: Augmented bilinear network for incremental multi-stock time-series classification. Pattern Recogn. 141, 109604 (2023)","journal-title":"Pattern Recogn."},{"key":"10_CR21","doi-asserted-by":"crossref","unstructured":"de\u00a0Souza, V.M.A., dos Reis, D.M., Maletzke, A.G., Batista, G.E.A.P.A.: Challenges in benchmarking stream learning algorithms with real-world data. Data Min. Knowl. Discov. 34(6), 1805\u20131858 (2020)","DOI":"10.1007\/s10618-020-00698-5"},{"issue":"1\u20132","key":"10_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3233\/DS-220057","volume":"6","author":"G Ziffer","year":"2023","unstructured":"Ziffer, G., Bernardo, A., Della Valle, E., Cerqueira, V., Bifet, A.: Towards time-evolving analytics: Online learning for time-dependent evolving data streams. Data Sci. 6(1\u20132), 1\u201316 (2023)","journal-title":"Data Sci."},{"key":"10_CR23","doi-asserted-by":"crossref","unstructured":"Ziffer, G., Giannini, F., Della Valle, E.: Tenet: Benchmarking Data Stream Classifiers in Presence of Temporal Dependence. In: 2024 IEEE International Conference on Big Data (BigData). pp. 1187\u20131196 (2024). 10.1109\/BigData62323.2024.10825670","DOI":"10.1109\/BigData62323.2024.10825670"},{"issue":"3","key":"10_CR24","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1007\/s10994-014-5441-4","volume":"98","author":"I Zliobaite","year":"2015","unstructured":"Zliobaite, I., Bifet, A., Read, J., Pfahringer, B., Holmes, G.: Evaluation methods and decision theory for classification of streaming data with temporal dependence. Mach. Learn. 98(3), 455\u2013482 (2015)","journal-title":"Mach. Learn."}],"container-title":["Communications in Computer and Information Science","Machine Learning and Principles and Practice of Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-19102-1_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T22:16:19Z","timestamp":1778364979000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-19102-1_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032191014","9783032191021"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-19102-1_10","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"10 May 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Porto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecmlpkdd.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}