{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:10:17Z","timestamp":1767319817293,"version":"3.48.0"},"publisher-location":"Singapore","reference-count":12,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819538294","type":"print"},{"value":"9789819538300","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\/978-981-95-3830-0_38","type":"book-chapter","created":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:07:38Z","timestamp":1767319658000},"page":"517-526","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SCFormer: Structured Channel-Wise Transformer with\u00a0Cumulative Historical State for\u00a0Multivariate Time Series Forecasting"],"prefix":"10.1007","author":[{"given":"Shiwei","family":"Guo","sequence":"first","affiliation":[]},{"given":"Ziang","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Yupeng","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Yunfei","family":"Han","sequence":"additional","affiliation":[]},{"given":"Yi","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,2]]},"reference":[{"key":"38_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.101819","volume":"97","author":"Z Chen","year":"2023","unstructured":"Chen, Z., Ma, M., Li, T., Wang, H., Li, C.: Long sequence time-series forecasting with deep learning: a survey. Inform. Fusion 97, 101819 (2023)","journal-title":"Inform. Fusion"},{"key":"38_CR2","unstructured":"Das, A., Kong, W., Leach, A., Mathur, S.K., Sen, R., Yu, R.: Long-term forecasting with tide: time-series dense encoder. Trans. Mach. Learn. Res. (2023)"},{"key":"38_CR3","first-page":"1474","volume":"33","author":"A Gu","year":"2020","unstructured":"Gu, A., Dao, T., Ermon, S., Rudra, A., R\u00e9, C.: HiPPO: recurrent memory with optimal polynomial projections. Adv. Neural. Inf. Process. Syst. 33, 1474\u20131487 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"38_CR4","unstructured":"Li, Z., Qi, S., Li, Y., Xu, Z.: Revisiting long-term time series forecasting: an investigation on linear mapping. arXiv preprint arXiv:2305.10721 (2023)"},{"key":"38_CR5","first-page":"5816","volume":"35","author":"M Liu","year":"2022","unstructured":"Liu, M., et al.: SCINet: time series modeling and forecasting with sample convolution and interaction. Adv. Neural. Inf. Process. Syst. 35, 5816\u20135828 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"38_CR6","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.patrec.2022.05.010","volume":"160","author":"Y Liu","year":"2022","unstructured":"Liu, Y., Wang, Z., Yu, X., Chen, X., Sun, M.: Memory-based transformer with shorter window and longer horizon for multivariate time series forecasting. Pattern Recogn. Lett. 160, 26\u201333 (2022)","journal-title":"Pattern Recogn. Lett."},{"key":"38_CR7","unstructured":"Liu, Y., et al.: iTransformer: inverted transformers are effective for time series forecasting. arXiv preprint arXiv:2310.06625 (2024)"},{"key":"38_CR8","unstructured":"Nie, Y., Nguyen, N.H., Sinthong, P., Kalagnanam, J.: A time series is worth 64 words: long-term forecasting with transformers. In: The Eleventh International Conference on Learning Representations (2022)"},{"key":"38_CR9","unstructured":"Wu, H., Hu, T., Liu, Y., Zhou, H., Wang, J., Long, M.: TimesNet: temporal 2D-variation modeling for general time series analysis. In: The Eleventh International Conference on Learning Representations (2022)"},{"key":"38_CR10","doi-asserted-by":"crossref","unstructured":"Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a037, pp. 11121\u201311128 (2023)","DOI":"10.1609\/aaai.v37i9.26317"},{"key":"38_CR11","unstructured":"Zhang, Y., Yan, J.: Crossformer: transformer utilizing cross-dimension dependency for multivariate time series forecasting. In: The Eleventh International Conference on Learning Representations (2022)"},{"key":"38_CR12","unstructured":"Zhou, T., Ma, Z., Wen, Q., Wang, X., Sun, L., Jin, R.: FEDformer: frequency enhanced decomposed transformer for long-term series forecasting. In: International Conference on Machine Learning, pp. 27268\u201327286. PMLR (2022)"}],"container-title":["Lecture Notes in Computer Science","Database Systems for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-3830-0_38","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:07:39Z","timestamp":1767319659000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-3830-0_38"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819538294","9789819538300"],"references-count":12,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-3830-0_38","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":"2 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DASFAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database Systems for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","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":"26 May 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 May 2025","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":"dasfaa2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/dasfaa2025.github.io","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}