{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T20:08:06Z","timestamp":1779912486166,"version":"3.53.1"},"publisher-location":"Singapore","reference-count":23,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819698745","type":"print"},{"value":"9789819698752","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-981-96-9875-2_31","type":"book-chapter","created":{"date-parts":[[2025,7,21]],"date-time":"2025-07-21T09:19:49Z","timestamp":1753089589000},"page":"366-377","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["CEDMix: Contrast-Enhanced Dynamic Channel Mixing for Correlated Time Series Forecasting"],"prefix":"10.1007","author":[{"given":"Kaixin","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Han","family":"Bao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yijie","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohui","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yizhou","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junhui","family":"Kan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chongxiang","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,22]]},"reference":[{"issue":"4","key":"31_CR1","doi-asserted-by":"publisher","first-page":"971","DOI":"10.14778\/3503585.3503604","volume":"15","author":"X Wu","year":"2022","unstructured":"Wu, X., Zhang, D., Guo, C., et al.: AutoCTS: automated correlated time series forecasting. Proc. VLDB Endow. 15(4), 971\u2013983 (2022)","journal-title":"Proc. VLDB Endow."},{"key":"31_CR2","doi-asserted-by":"crossref","unstructured":"Zhang, W., Zhang, L., Han, J., et al.: Irregular traffic time series forecasting based on asynchronous spatio-temporal graph convolutional networks. In: Proceedings 30th ACM SIGKDD Conference Knowledge Discovery Data Mining, pp. 4302\u20134313 (2024)","DOI":"10.1145\/3637528.3671665"},{"key":"31_CR3","doi-asserted-by":"crossref","unstructured":"Han, L., Chen, X., Ye, H., et al.: SoftS: efficient multivariate time series forecasting with series-core fusion. arXiv preprint arXiv:2404.14197 (2024)","DOI":"10.52202\/079017-2046"},{"issue":"4","key":"31_CR4","doi-asserted-by":"publisher","first-page":"753","DOI":"10.14778\/3636218.3636230","volume":"17","author":"K Zhao","year":"2023","unstructured":"Zhao, K., Guo, C., Cheng, Y., et al.: Multiple time series forecasting with dynamic graph modeling. Proc. VLDB Endow. 17(4), 753\u2013765 (2023)","journal-title":"Proc. VLDB Endow."},{"key":"31_CR5","unstructured":"Nie, Y., Nguyen, N., Sinthong, P., et al.: A time series is worth 64 words: long term forecasting with transformers. arXiv preprint arXiv:2211.14730 (2022)"},{"key":"31_CR6","doi-asserted-by":"crossref","unstructured":"Zeng, A., Chen, M., Zhang, L., et al.: Are transformers effective for time series forecasting? In: Proceedings AAAI Conference Artificial Intelligence, vol. 37, pp. 11121\u201311128 (2023)","DOI":"10.1609\/aaai.v37i9.26317"},{"key":"31_CR7","unstructured":"Lin, S., Lin, W., Wu, W., et al.: SparseTSF: modeling long-term time series forecasting with 1k parameters. arXiv preprint arXiv:2405.00946 (2024)"},{"key":"31_CR8","first-page":"46885","volume":"36","author":"Q Huang","year":"2023","unstructured":"Huang, Q., Shen, L., Zhang, R., et al.: CrossGNN: confronting noisy multivariate time series via cross interaction refinement. Adv. Neural. Inf. Process. Syst. 36, 46885\u201346902 (2023)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"31_CR9","unstructured":"Chen, P., Zhang, Y., Cheng, Y., et al.: Pathformer: multi-scale transformers with adaptive pathways for time series forecasting. arXiv preprint arXiv:2402.05956 (2024)"},{"key":"31_CR10","doi-asserted-by":"publisher","first-page":"130635","DOI":"10.52202\/079017-4152","volume":"37","author":"J Chen","year":"2024","unstructured":"Chen, J., Lenssen, J., Feng, A., et al.: From similarity to superiority: channel clustering for time series forecasting. Adv. Neural. Inf. Process. Syst. 37, 130635\u2013130663 (2024)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"31_CR11","unstructured":"Liu, Q., Fang, Y., Jiang, P., et al.: DGCformer: deep graph clustering transformer for multivariate time series forecasting. arXiv preprint arXiv:2405.08440 (2024)"},{"key":"31_CR12","doi-asserted-by":"publisher","first-page":"7129","DOI":"10.1109\/TKDE.2024.3400008","volume":"36","author":"L Han","year":"2024","unstructured":"Han, L., Ye, H., Zhan, D.: The capacity and robustness trade-off: revisiting the channel independent strategy for multivariate time series forecasting. IEEE Trans. Knowl. Data Eng. 36, 7129\u20137142 (2024)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"31_CR13","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., et al.: Inception-v4, inception-ResNet and the impact of residual connections on learning. In: Proceedings AAAI Conference Artificial Intelligence, vol. 31 (2017)","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"31_CR14","doi-asserted-by":"crossref","unstructured":"Liang, Y., Ning, J., Li, Y., et al.: Quantification of localization uncertainty in one-stage object detection. In: International Conference on Intelligent Information Technologies for Industry, pp. 23\u201336. Springer (2023)","DOI":"10.1007\/978-3-031-43792-2_3"},{"issue":"8","key":"31_CR15","doi-asserted-by":"publisher","first-page":"7067","DOI":"10.1016\/j.eswa.2012.01.039","volume":"39","author":"S Taieb","year":"2012","unstructured":"Taieb, S., Bontempi, G., Atiya, A., et al.: A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition. Expert Syst. Appl. 39(8), 7067\u20137083 (2012)","journal-title":"Expert Syst. Appl."},{"key":"31_CR16","unstructured":"Gao, W., Hu, T.: OpenI QiZhi - a new generation open source and open platform for artificial intelligence. https:\/\/openi.org.cn\/. Accessed 9 April 2025"},{"key":"31_CR17","unstructured":"Peng Cheng Laboratory: Peng Cheng Cloud Brain. https:\/\/cloudbrain.pcl.ac.cn\/. Accessed 9 April 2025"},{"key":"31_CR18","unstructured":"Lu, J., Han, X., Sun, Y., et al.: CATS: enhancing multivariate time series forecasting by constructing auxiliary time series as exogenous variables. In: International Conference Machine Learning, pp. 32990\u201333006. PMLR (2024)"},{"key":"31_CR19","doi-asserted-by":"crossref","unstructured":"Yu, C., Wang, F., Shao, Z., et al.: DSformer: a double sampling transformer for multivariate time series long-term prediction. In: Proceedings 32nd ACM International Conference Information Knowledge Management, pp. 3062\u20133072 (2023)","DOI":"10.1145\/3583780.3614851"},{"key":"31_CR20","doi-asserted-by":"crossref","unstructured":"Tang, P., Zhang, W.: Unlocking the power of patch: patch-based MLP for long-term time series forecasting. arXiv preprint arXiv:2405.13575 (2024)","DOI":"10.1609\/aaai.v39i12.33378"},{"key":"31_CR21","doi-asserted-by":"crossref","unstructured":"Zhou, H., Zhang, S., Peng, J., et al.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings AAAI Conference Artificial Intelligence, vol. 35, pp. 11106\u201311115 (2021)","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"31_CR22","doi-asserted-by":"crossref","unstructured":"Shao, Z., Zhang, Z., Wang, F., et al.: Spatial-temporal identity: a simple yet effective baseline for multivariate time series forecasting. In: Proceedings 31st ACM International Conference Information Knowledge Management, pp. 4454\u20134458 (2022)","DOI":"10.1145\/3511808.3557702"},{"key":"31_CR23","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1109\/TKDE.2024.3484454","volume":"37","author":"Z Shao","year":"2024","unstructured":"Shao, Z., Wang, F., Xu, Y., et al.: Exploring progress in multivariate time series forecasting: comprehensive benchmarking and heterogeneity analysis. IEEE Trans. Knowl. Data Eng. 37, 291\u2013305 (2024)","journal-title":"IEEE Trans. Knowl. Data Eng."}],"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\/978-981-96-9875-2_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T19:45:43Z","timestamp":1779911143000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-9875-2_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819698745","9789819698752"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-9875-2_31","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"22 July 2025","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":"Ningbo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/icg\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}