{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T05:43:57Z","timestamp":1771566237686,"version":"3.50.1"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032155344","type":"print"},{"value":"9783032155351","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\/978-3-032-15535-1_12","type":"book-chapter","created":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T04:51:53Z","timestamp":1771563113000},"page":"181-195","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["FiTEM: Fine-Tuning Time-Series Foundation Models for\u00a0Selective Forecasting"],"prefix":"10.1007","author":[{"given":"Jonas","family":"Brusokas","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seshu","family":"Tirupathi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Torben Bach","family":"Pedersen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,21]]},"reference":[{"key":"12_CR1","unstructured":"Brusokas, J., Tirupathi, S., Zhang, D., Pedersen, T.B.: The time-energy model: selective time-series forecasting using energy-based models. Trans. Mach. Learn. Res. (2025). https:\/\/openreview.net\/forum?id=iHYCdTAOqF"},{"key":"12_CR2","unstructured":"Das, A., et al.: A decoder-only foundation model for time-series forecasting. In: Proceedings of the 41st International Conference on Machine Learning (ICML) (2024)"},{"key":"12_CR3","unstructured":"Ekambaram, V., et al.: Tiny time mixers (TTMS): fast pre-trained models for enhanced zero\/few-shot forecasting of multivariate time series (2024)"},{"key":"12_CR4","unstructured":"Geifman, Y., El-Yaniv, R.: Selectivenet: a deep neural network with an integrated reject option. In: International Conference on Machine Learning (2019)"},{"key":"12_CR5","unstructured":"Gruver, N., Finzi, M.A., Qiu, S., Wilson, A.G.: Large Language Models Are Zero-Shot Time Series Forecasters (2023). https:\/\/openreview.net\/forum?id=md68e8iZK1"},{"issue":"8","key":"12_CR6","doi-asserted-by":"publisher","first-page":"1771","DOI":"10.1162\/089976602760128018","volume":"14","author":"GE Hinton","year":"2002","unstructured":"Hinton, G.E.: Training products of experts by minimizing contrastive divergence. Neural Comput. 14(8), 1771\u20131800 (2002). https:\/\/doi.org\/10.1162\/089976602760128018","journal-title":"Neural Comput."},{"key":"12_CR7","unstructured":"Jin, M., et al.: Time-LLM: Time Series Forecasting by Reprogramming Large Language Models (2023). https:\/\/openreview.net\/forum?id=Unb5CVPtae"},{"key":"12_CR8","doi-asserted-by":"publisher","unstructured":"Li, Z., et al.: FoundTS: Comprehensive and Unified Benchmarking of Foundation Models for Time Series Forecasting (2024). https:\/\/doi.org\/10.48550\/arXiv.2410.11802, [cs]","DOI":"10.48550\/arXiv.2410.11802"},{"key":"12_CR9","doi-asserted-by":"publisher","unstructured":"Liang, Y., et al.: Foundation models for time series analysis: a tutorial and survey. In: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2024, pp. 6555\u20136565. Association for Computing Machinery, New York (2024). https:\/\/doi.org\/10.1145\/3637528.3671451","DOI":"10.1145\/3637528.3671451"},{"key":"12_CR10","unstructured":"Liu, Y., Qin, G., Huang, X., Wang, J., Long, M.: Timer-xl: long-context transformers for unified time series forecasting. In: International Conference on Learning Representations (ICLR) (2025). https:\/\/arxiv.org\/abs\/2410.04803"},{"key":"12_CR11","unstructured":"Liu, Y., Zhang, H., Li, C., Huang, X., Wang, J., Long, M.: Timer: generative pre-trained transformers are large time series models. In: Forty-first International Conference on Machine Learning (2024)"},{"key":"12_CR12","doi-asserted-by":"publisher","unstructured":"Nie, Y., Nguyen, N.H., Sinthong, P., Kalagnanam, J.: A time series is worth 64 words: long-term forecasting with transformers (2023). https:\/\/doi.org\/10.48550\/arXiv.2211.14730","DOI":"10.48550\/arXiv.2211.14730"},{"key":"12_CR13","unstructured":"Tan, M., Merrill, M.A., Gupta, V., Althoff, T., Hartvigsen, T.: Are language models actually useful for time series forecasting? In: Advances in Neural Information Processing Systems (NeurIPS) (2024). https:\/\/arxiv.org\/abs\/2406.16964, spotlight"},{"key":"12_CR14","unstructured":"Wu, H., Hu, T., Liu, Y., Zhou, H., Wang, J., Long, M.: Timesnet: temporal 2D-variation modeling for general time series analysis. In: International Conference on Learning Representations (2023). https:\/\/arxiv.org\/abs\/2210.02186"},{"key":"12_CR15","unstructured":"Wu, H., Xu, J., Wang, J., Long, M.: Autoformer: decomposition Transformers with auto-correlation for long-term series forecasting. In: Advances in Neural Information Processing Systems, vol.\u00a034, pp. 22419\u201322430. Curran Associates, Inc. (2021)"},{"key":"12_CR16","doi-asserted-by":"publisher","unstructured":"Zhang, X.Y., Xie, G.S., Li, X., Mei, T., Liu, C.L.: A survey on learning to reject. 111(2), 185\u2013215 (2023). https:\/\/doi.org\/10.1109\/JPROC.2023.3238024, conference Name: Proceedings of the IEEE","DOI":"10.1109\/JPROC.2023.3238024"},{"key":"12_CR17","doi-asserted-by":"publisher","unstructured":"Zhou, H., et al.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a035, pp. 11106\u201311115 (2021). https:\/\/doi.org\/10.1609\/aaai.v35i12.17325","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"12_CR18","unstructured":"Zhou, T., Ma, Z., Wen, Q., Wang, X., Sun, L., Jin, R.: FEDformer: frequency enhanced decomposed transformer for long-term series forecasting. In: Proceedings of the 39th International Conference on Machine Learning, pp. 27268\u201327286. PMLR (2022)"},{"key":"12_CR19","unstructured":"Zhou, T., Niu, P., Wang, X., Sun, L., Jin, R.: One fits all: power general time series analysis by pretrained LM. In: Proceedings of the 37th International Conference on Neural Information Processing Systems, NIPS 2023, Curran Associates Inc., Red Hook, NY, USA (2023)"}],"container-title":["Lecture Notes in Computer Science","Advanced Analytics and Learning on Temporal Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-15535-1_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T04:51:56Z","timestamp":1771563116000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-15535-1_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032155344","9783032155351"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-15535-1_12","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"21 February 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"AALTD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Advanced Analytics and Learning on Temporal Data","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":"19 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":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aaltd2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecml-aaltd.github.io\/aaltd2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}