{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T19:32:27Z","timestamp":1757619147471,"version":"3.44.0"},"publisher-location":"Singapore","reference-count":22,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819698745"},{"type":"electronic","value":"9789819698752"}],"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_11","type":"book-chapter","created":{"date-parts":[[2025,7,21]],"date-time":"2025-07-21T09:22:10Z","timestamp":1753089730000},"page":"120-131","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["IDMixer: Decomposition Spatial-Temporal Identity for Traffic Flow Forecasting"],"prefix":"10.1007","author":[{"given":"Kaiqi","family":"Wu","sequence":"first","affiliation":[]},{"given":"Sen","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yubao","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,22]]},"reference":[{"key":"11_CR1","unstructured":"Bai, L., Yao, L., Li, C., et al.: Adaptive graph convolutional recurrent network for traffic forecasting. In: Advances in Neural Information Processing Systems (NeurIPS) (2020)"},{"issue":"1","key":"11_CR2","first-page":"3","volume":"6","author":"RB Cleveland","year":"1990","unstructured":"Cleveland, R.B., Cleveland, W.S., McRae, J.E., et al.: STL: a seasonal-trend decomposition. J. Off. Stat. 6(1), 3\u201373 (1990)","journal-title":"J. Off. Stat."},{"key":"11_CR3","unstructured":"Deng, J., Chen, X., Jiang, R., et al.: Spatial and temporal normalization for multivariate time series forecasting. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (SIGKDD) (2021)"},{"key":"11_CR4","doi-asserted-by":"crossref","unstructured":"Fang, Y., Qin, Y., Luo, H., et al.: When spatio-temporal meet wavelets: Disentangled traffic forecasting via efficient spectral graph attention networks. In: IEEE International Conference on Data Engineering (ICDE) (2023)","DOI":"10.1109\/ICDE55515.2023.00046"},{"key":"11_CR5","doi-asserted-by":"crossref","unstructured":"Fang, Z., Long, Q., Song, G., et al.: Spatial-temporal graph ODE networks for traffic flow forecasting. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (SIGKDD) (2021)","DOI":"10.1145\/3447548.3467430"},{"key":"11_CR6","doi-asserted-by":"publisher","DOI":"10.2307\/j.ctv14jx6sm","volume-title":"Time Series Analysis","author":"JD Hamilton","year":"2020","unstructured":"Hamilton, J.D.: Time Series Analysis. Princeton University Press (2020)"},{"key":"11_CR7","doi-asserted-by":"crossref","unstructured":"Huang, R., Huang, C., Liu, Y., et al.: LSGCN: Long short-term traffic prediction with graph convolutional networks. In: International Joint Conference on Artificial Intelligence (IJCAI) (2020)","DOI":"10.24963\/ijcai.2020\/326"},{"key":"11_CR8","doi-asserted-by":"crossref","unstructured":"Jiang, J., Han, C., Zhao, W.X., et al.: Propagation delay-aware dynamic long-range transformer for traffic flow prediction. In: AAAI Conference on Artificial Intelligence (AAAI) (2023)","DOI":"10.1609\/aaai.v37i4.25556"},{"key":"11_CR9","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations (ICLR) (2017)"},{"key":"11_CR10","doi-asserted-by":"crossref","unstructured":"Kong, W., Guo, Z., et al.: Spatio-temporal pivotal graph neural networks for traffic flow forecasting. In: AAAI Conference on Artificial Intelligence (AAAI) (2024)","DOI":"10.1609\/aaai.v38i8.28707"},{"issue":"1","key":"11_CR11","first-page":"1","volume":"17","author":"F Li","year":"2023","unstructured":"Li, F., Feng, J., Yan, H., et al.: Dynamic graph convolutional recurrent network for traffic prediction: benchmark and solution. ACM Trans. Knowl. Discov. Data 17(1), 1\u201321 (2023)","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"11_CR12","doi-asserted-by":"crossref","unstructured":"Li, M., Zhu, Z.: Spatial-temporal fusion graph neural networks for traffic flow forecasting. In: AAAI Conference on Artificial Intelligence (AAAI) (2021)","DOI":"10.1609\/aaai.v35i5.16542"},{"key":"11_CR13","unstructured":"Li, Y., Yu, R., Shahabi, C., et al.: Diffusion convolutional recurrent neural network: data-driven traffic forecasting. In: International Conference on Learning Representations (ICLR) (2018)"},{"key":"11_CR14","doi-asserted-by":"crossref","unstructured":"Liu, H., Dong, Z., Jiang, R., et al.: STAEformer: Spatio-temporal adaptive embedding makes vanilla transformer SOTA for traffic forecasting. In: ACM International Conference on Information and Knowledge Management (CIKM) (2023)","DOI":"10.1145\/3583780.3615160"},{"key":"11_CR15","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: ACM International Conference on Information and Knowledge Management (CIKM) (2022)","DOI":"10.1145\/3511808.3557702"},{"key":"11_CR16","doi-asserted-by":"crossref","unstructured":"Shao, Z., Zhang, Z., Wei, W., et al.: Decoupled dynamic spatial-temporal graph neural network for traffic forecasting. In: Very Large Data Bases (VLDB) (2022)","DOI":"10.14778\/3551793.3551827"},{"key":"11_CR17","doi-asserted-by":"crossref","unstructured":"Yu, B., Yin, H., Zhu, Z.: Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. In: International Joint Conference on Artificial Intelligence (IJCAI) (2018)","DOI":"10.24963\/ijcai.2018\/505"},{"key":"11_CR18","unstructured":"Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(11) (2008)"},{"key":"11_CR19","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems (NeurIPS) (2017)"},{"key":"11_CR20","unstructured":"Wu, H., Xu, J., Wang, J., et al.: Decomposition transformers with auto-correlation for long-term series forecasting. In: Advances in Neural Information Processing Systems (NeurIPS) (2021)"},{"key":"11_CR21","doi-asserted-by":"crossref","unstructured":"Wu, Z., Pan, S., Long, G., et al.: Connecting the dots: multivariate time series forecasting with graph neural networks. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (SIGKDD) (2020)","DOI":"10.1145\/3394486.3403118"},{"key":"11_CR22","doi-asserted-by":"crossref","unstructured":"Wu, Z., Pan, S., Long, G., et al.: Graph WaveNet for deep spatial-temporal graph modeling. In: International Joint Conference on Artificial Intelligence (IJCAI) (2019)","DOI":"10.24963\/ijcai.2019\/264"}],"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_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,7]],"date-time":"2025-09-07T16:18:54Z","timestamp":1757261934000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-9875-2_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819698745","9789819698752"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-9875-2_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"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"}}]}}