{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T14:18:36Z","timestamp":1783952316433,"version":"3.55.0"},"publisher-location":"Singapore","reference-count":30,"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_4","type":"book-chapter","created":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T13:25:14Z","timestamp":1783949114000},"page":"39-50","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SFAT-Net: Non-stationary Time Series Forecasting with Multi-resolution Temporal Embedding and Adaptive Alignment"],"prefix":"10.1007","author":[{"given":"Yan","family":"Su","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinlai","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dazhi","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinmei","family":"Qi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuhan","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,14]]},"reference":[{"key":"4_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2026.123535","volume":"749","author":"F Oueslati","year":"2026","unstructured":"Oueslati, F., Abbes, A.B., Barra, V.: Fourier-optimal loss for distortion and time in non-stationary time series forecasting. Inf. Sci. 749, 123535 (2026)","journal-title":"Inf. Sci."},{"key":"4_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2026.123441","volume":"747","author":"S Wen","year":"2026","unstructured":"Wen, S., Wang, N., Liu, R., Wang, R., Zhong, Y.: FPS: frequency-aware polynomial spectral reconstruction for dual-domain learning in long-term time series forecasting. Inf. Sci. 747, 123441 (2026)","journal-title":"Inf. Sci."},{"key":"4_CR3","first-page":"1","volume-title":"International Conference on Learning Representations (ICLR)","author":"Y Liu","year":"2024","unstructured":"Liu, Y., et al.: iTransformer: inverted transformers are effective for time series forecasting. In: International Conference on Learning Representations (ICLR), pp. 1\u201318 (2024)"},{"key":"4_CR4","unstructured":"Nie, Y., Nguyen, N.H., Sinthong, P., Kalagnanam, J.: A time series is worth 64 words: long-term forecasting with transformers. arXiv:2211.14730. (2023)"},{"key":"4_CR5","first-page":"19245","volume-title":"The Thirty-Eighth Annual Conference on Neural Information Processing Systems","author":"D Luo","year":"2024","unstructured":"Luo, D., Wang, X.: DeformableTST: transformer for time series forecasting without over-reliance on patching. In: The Thirty-Eighth Annual Conference on Neural Information Processing Systems, pp. 19245\u201319258 (2024)"},{"key":"4_CR6","first-page":"1","volume-title":"International Conference on Learning Representations (ICLR)","author":"D Luo","year":"2024","unstructured":"Luo, D., Wang, X.: ModernTCN: a modern pure convolution structure for general time series analysis. In: International Conference on Learning Representations (ICLR), pp. 1\u201316 (2024)"},{"key":"4_CR7","first-page":"1","volume-title":"International Conference on Learning Representations (ICLR)","author":"S Wang","year":"2024","unstructured":"Wang, S., et al.: TimeMixer: decomposable multiscale mixing for time series forecasting. In: International Conference on Learning Representations (ICLR), pp. 1\u201315 (2024)"},{"key":"4_CR8","unstructured":"Liu, P., et al.: TimeBridge: non-stationarity matters for long-term time series forecasting. arXiv:2410.04442. (2025)"},{"key":"4_CR9","first-page":"55115","volume-title":"Advances in Neural Information Processing Systems","author":"K Yi","year":"2024","unstructured":"Yi, K., et al.: FilterNet: harnessing frequency filters for time series forecasting. In: Globerson, A., et al. (eds.) Advances in Neural Information Processing Systems, vol. 37, pp. 55115\u201355140. Curran Associates, Inc. (2024)"},{"key":"4_CR10","first-page":"3606","volume-title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25","author":"W Yue","year":"2025","unstructured":"Yue, W., Liu, Y., Ying, X., Xing, B., Guo, R., Shi, J.: FreEformer: frequency enhanced transformer for multivariate time series forecasting. In: Kwok, J. (ed.) Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25, pp. 3606\u20133614. International Joint Conferences on Artificial Intelligence Organization (2025)"},{"issue":"18","key":"4_CR11","first-page":"19581","volume":"39","author":"MMN Murad","year":"2025","unstructured":"Murad, M.M.N., Aktukmak, M., Yilmaz, Y.: WPMixer: efficient multi-resolution mixing for long-term time series forecasting. Proc. AAAI Conf. Artif. Intell. 39(18), 19581\u201319588 (2025)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"4_CR12","first-page":"27268","volume-title":"Proceedings of the 39th International Conference on Machine Learning, Proceedings of Machine Learning Research","author":"T Zhou","year":"2022","unstructured":"Zhou, T., Ma, Z., Wen, Q., Wang, X., Sun, L., Jin, R.: FEDformer: frequency enhanced decomposed transformer for long-term series forecasting. In: Chaudhuri, K., Jegelka, S., Song, L., Szepesvari, C., Niu, G., Sabato, S. (eds.) Proceedings of the 39th International Conference on Machine Learning, Proceedings of Machine Learning Research, vol. 162, p. 27268. PMLR, 27286 (2022)"},{"key":"4_CR13","first-page":"1","volume-title":"The Eleventh International Conference on Learning Representations","author":"Y Zhang","year":"2023","unstructured":"Zhang, Y., Yan, J.: Crossformer: transformer utilizing cross-dimension dependency for multivariate time series forecasting. In: The Eleventh International Conference on Learning Representations, pp. 1\u201314 (2023)"},{"key":"4_CR14","unstructured":"Yu, G., Zou, J., Hu, X., Aviles-Rivero, A.I., Qin, J., Wang, S.: Revitalizing multivariate time series forecasting: learnable decomposition with inter-series dependencies and intra-series variations modeling. arXiv:2402.12694. (2024)"},{"key":"4_CR15","first-page":"1","volume-title":"The Eleventh International Conference on Learning Representations","author":"H Wang","year":"2023","unstructured":"Wang, H., Peng, J., Huang, F., Wang, J., Chen, J., Xiao, Y.: MICN: multi-scale local and global context modeling for long-term series forecasting. In: The Eleventh International Conference on Learning Representations, pp. 1\u201312 (2023)"},{"key":"4_CR16","unstructured":"Wu, H., Hu, T., Liu, Y., Zhou, H., Wang, J., Long, M.: TimesNet: temporal 2D-variation modeling for general time series analysis. arXiv:2210.02186. (2023)"},{"issue":"9","key":"4_CR17","first-page":"11121","volume":"37","author":"A Zeng","year":"2023","unstructured":"Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? Proc. AAAI Conf. Artif. Intell. 37(9), 11121\u201311128 (2023)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"4_CR18","doi-asserted-by":"crossref","unstructured":"Liu, M., et al.: SCINet: time series modeling and forecasting with sample convolution and interaction. arXiv:2106.09305. (2022)","DOI":"10.52202\/068431-0421"},{"key":"4_CR19","doi-asserted-by":"crossref","unstructured":"Qiu, X., Wu, X., Lin, Y., Guo, C., Hu, J., Yang, B.: DUET: dual clustering enhanced multivariate time series forecasting. arXiv:2412.10859. (2025)","DOI":"10.1145\/3690624.3709325"},{"key":"4_CR20","first-page":"8024","volume-title":"Advances in Neural Information Processing Systems","author":"A Paszke","year":"2019","unstructured":"Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, pp. 8024\u20138035 (2019)"},{"key":"4_CR21","first-page":"1","volume-title":"International Conference on Learning Representations (ICLR)","author":"DP Kingma","year":"2015","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (ICLR), pp. 1\u201313 (2015)"},{"key":"4_CR22","doi-asserted-by":"crossref","unstructured":"Wang, H., et al.: Fredf: learning to forecast in the frequency domain. arXiv:2402.02399. (2025)","DOI":"10.1201\/9781003612742-3"},{"key":"4_CR23","first-page":"15274","volume-title":"Advances in Neural Information Processing Systems","author":"H Wu","year":"2021","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, pp. 15274\u201315285 (2021)"},{"key":"4_CR24","doi-asserted-by":"crossref","unstructured":"Miao, H., et al.: Less is more: efficient time series dataset condensation via two-fold modal matching\u2013extended version. arXiv:2410.20905. (2024)","DOI":"10.14778\/3705829.3705841"},{"key":"4_CR25","doi-asserted-by":"crossref","unstructured":"Liu, C., et al.: Efficient multivariate time series forecasting via calibrated language models with privileged knowledge distillation. arXiv:2505.02138. (2025)","DOI":"10.1109\/ICDE65448.2025.00237"},{"key":"4_CR26","doi-asserted-by":"crossref","unstructured":"Liu, C., et al.: TimeCMA: towards LLM-empowered multivariate time series forecasting via cross-modality alignment. arXiv:2406.01638. (2025)","DOI":"10.1609\/aaai.v39i18.34067"},{"key":"4_CR27","unstructured":"Zhou, H., et al.: Informer: beyond efficient transformer for long sequence time-series forecasting. CoRR abs\/2012.07436. (2020)"},{"key":"4_CR28","first-page":"7262","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Y Li","year":"2025","unstructured":"Li, Y., Yang, C., Zeng, H., et al.: Frequency-aligned knowledge distillation for lightweight spatiotemporal forecasting. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 7262\u20137272 (2025)"},{"issue":"38","key":"4_CR29","first-page":"31861","volume":"40","author":"Y Li","year":"2026","unstructured":"Li, Y., Li, K., Yin, X., et al.: Sepprune: structured pruning for efficient deep speech separation. Proc. AAAI Conf. Artif. Intell. 40(38), 31861\u201331869 (2026)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"4_CR30","doi-asserted-by":"crossref","unstructured":"Li, Y., Meng, S., Yang, C., et al.: A comprehensive survey of interaction techniques in 3D scene generation. Authorea Preprints. (2026)","DOI":"10.22541\/au.177083718.88659470\/v1"}],"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_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T13:25:18Z","timestamp":1783949118000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-3384-7_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,14]]},"ISBN":["9789819233830","9789819233847"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-3384-7_4","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"}}]}}