{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T14:20:10Z","timestamp":1783952410405,"version":"3.55.0"},"publisher-location":"Singapore","reference-count":26,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819233830","type":"print"},{"value":"9789819233847","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T00:00:00Z","timestamp":1783987200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T00:00:00Z","timestamp":1783987200000},"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\/978-981-92-3384-7_10","type":"book-chapter","created":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T13:24:43Z","timestamp":1783949083000},"page":"110-120","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SA-MoE: Spectral-Aware Mixture of Experts for Long-Term Time Series Forecasting"],"prefix":"10.1007","author":[{"given":"Boran","family":"Duan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wang","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rongwei","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,14]]},"reference":[{"key":"10_CR1","unstructured":"Ansari, F.A., et al.: Chronos: learning the language of time series. TMLR. (2024)"},{"key":"10_CR2","unstructured":"Chen, S.A., Li, C.L., Arik, S.O., Yoder, N., Pfister, T.: TSMixer: An all-MLP architecture for time series forecasting. TMLR. (2023)"},{"key":"10_CR3","unstructured":"Das, A., Kong, W., Leach, A., Mathur, S., Sen, R., Yu, R.: Long-term forecasting with TiDE: Time-series dense encoder. TMLR. (2023)"},{"key":"10_CR4","unstructured":"Das, A., Kong, W., Sen, R., Zhou, Y.: A decoder-only foundation model for time-series forecasting. In: ICML (2024)"},{"key":"10_CR5","unstructured":"Fedus, W., Zoph, B., Shazeer, N.: Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity. JMLR. 23(120) (2022)"},{"key":"10_CR6","doi-asserted-by":"crossref","unstructured":"Jacobs, R.A., Jordan, M.I., Nowlan, S.J., Hinton, G.E.: Adaptive mixtures of local experts. Neural Comput. 3(1) (1991)","DOI":"10.1162\/neco.1991.3.1.79"},{"key":"10_CR7","unstructured":"Jin, M., et al.: Time-LLM: time series forecasting by reprogramming large language models. In: ICLR (2024)"},{"key":"10_CR8","unstructured":"Liu, Y., et al.: iTransformer: inverted transformers are effective for time series forecasting. In: ICLR (2024)"},{"key":"10_CR9","unstructured":"Liu, Y., et al.: Sundial: a family of highly capable time series foundation models. In: ICML (2025)"},{"key":"10_CR10","unstructured":"Liu, Y., Zhang, H., Li, C., Huang, X., Wang, J., Long, M.: Timer: Generative pre-trained transformers are large time series models. In: ICML (2024)"},{"key":"10_CR11","unstructured":"Luo, D., Wang, X.: ModernTCN: a modern pure convolution structure for general time series analysis. In: ICLR (2024)"},{"key":"10_CR12","unstructured":"Ni, R., Lin, Z., Wang, S., Fanti, G.: MoLE: mixture of linear experts for long-term time series forecasting. In: AISTATS (2024)"},{"key":"10_CR13","unstructured":"Nie, Y., Nguyen, N.H., Sinthong, P., Kalagnanam, J.: A time series is worth 64 words: long-term forecasting with transformers. In: ICLR (2023)"},{"key":"10_CR14","unstructured":"Shi, X., et al.: Time-MoE: billion-scale time series foundation models with mixture of experts. In: ICLR (2025)"},{"key":"10_CR15","unstructured":"Wang, S., et al.: TimeMixer++: a general time series pattern machine for universal predictive analysis. In: ICLR (2025)"},{"key":"10_CR16","unstructured":"Wang, S., et al.: TimeMixer: decomposable multiscale mixing for time series forecasting. In: ICLR (2024)"},{"key":"10_CR17","unstructured":"Liu, X., et al.: Moirai-MoE: Empowering time series foundation models with sparse mixture of experts. In: ICML (2025)"},{"key":"10_CR18","unstructured":"Woo, G., Liu, C., Kumar, A., Xiong, C., Savarese, S., Sahoo, D.: Unified training of universal time series forecasting transformers. In: ICML (2024)"},{"key":"10_CR19","unstructured":"Wu, H., Hu, T., Liu, Y., Zhou, H., Wang, J., Long, M.: TimesNet: temporal 2D-variation modeling for general time series analysis. In: ICLR (2023)"},{"key":"10_CR20","unstructured":"Wu, H., Xu, J., Wang, J., Long, M.: Autoformer: decomposition transformers with auto-correlation for long-term series forecasting. In: NeurIPS (2021)"},{"key":"10_CR21","doi-asserted-by":"crossref","unstructured":"Yi, K., et al.: Frequency-domain MLPs are more effective learners in time series forecasting. In: NeurIPS (2023)","DOI":"10.52202\/075280-3349"},{"key":"10_CR22","doi-asserted-by":"crossref","unstructured":"Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: AAAI (2023)","DOI":"10.1609\/aaai.v37i9.26317"},{"key":"10_CR23","unstructured":"Zhang, Y., Yan, J.: Crossformer: transformer utilizing cross-dimension dependency for multivariate time series forecasting. In: ICLR (2023)"},{"key":"10_CR24","doi-asserted-by":"crossref","unstructured":"Zhou, H., et al.: Informer: Beyond efficient transformer for long sequence time-series forecasting. In: AAAI (2021)","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"10_CR25","unstructured":"Zhou, T., Ma, Z., Wen, Q., Wang, X., Sun, L., Jin, R.: FEDformer: frequency enhanced decomposed transformer for long-term series forecasting. In: ICML (2022)"},{"key":"10_CR26","doi-asserted-by":"publisher","DOI":"10.1201\/b12207","volume-title":"Ensemble Methods: Foundations and Algorithms","author":"ZH Zhou","year":"2012","unstructured":"Zhou, Z.H.: Ensemble Methods: Foundations and Algorithms. CRC Press (2012)"}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-3384-7_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T13:24:47Z","timestamp":1783949087000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-3384-7_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,14]]},"ISBN":["9789819233830","9789819233847"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-3384-7_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,14]]},"assertion":[{"value":"14 July 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Toronto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","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":"22 July 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2026a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2026\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}