{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T13:51:44Z","timestamp":1781877104042,"version":"3.54.5"},"publisher-location":"Singapore","reference-count":28,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819538263","type":"print"},{"value":"9789819538270","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\/978-981-95-3827-0_9","type":"book-chapter","created":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T13:02:59Z","timestamp":1781874179000},"page":"136-152","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["STPformer: Mutation-Aware Spatial-Temporal Pivotal Attention Networks for\u00a0Transformer-Based Traffic Forecasting"],"prefix":"10.1007","author":[{"given":"Hongyang","family":"Su","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenyun","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingcai","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Beibei","family":"Kong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengxiang","family":"Zhuo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaolong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,20]]},"reference":[{"key":"9_CR1","unstructured":"Ahmed, M.S., Cook, A.R.: Analysis of Freeway Traffic Time-series Data by Using Box-Jenkins Techniques. No.\u00a0722 (1979)"},{"key":"9_CR2","unstructured":"Bai, L., Yao, L., Li, C., Wang, X., Wang, C.: Adaptive graph convolutional recurrent network for traffic forecasting. In: Neurips (2020)"},{"key":"9_CR3","unstructured":"Chen, Y., Segovia-Dominguez, I., Gel, Y.R.: Z-gcnets: Time zigzags at graph convolutional networks for time series forecasting. In: ICML. pp. 1684\u20131694 (2021)"},{"key":"9_CR4","doi-asserted-by":"crossref","unstructured":"Deng, J., Chen, X., Jiang, R., Song, X., Tsang, I.W.: St-norm: Spatial and temporal normalization for multi-variate time series forecasting. In: SIGKDD, pp. 269\u2013278 (2021)","DOI":"10.1145\/3447548.3467330"},{"key":"9_CR5","doi-asserted-by":"crossref","unstructured":"Guo, S., Lin, Y., Feng, N., Song, C., Wan, H.: Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In: AAAI, pp. 922\u2013929 (2019)","DOI":"10.1609\/aaai.v33i01.3301922"},{"key":"9_CR6","doi-asserted-by":"crossref","unstructured":"Huang, R., Huang, C., Liu, Y., Dai, G., Kong, W.: Lsgcn: Long short-term traffic prediction with graph convolutional networks. In: IJCAI, pp. 2355\u20132361 (2020)","DOI":"10.24963\/ijcai.2020\/326"},{"key":"9_CR7","doi-asserted-by":"crossref","unstructured":"Jiang, J., Han, C., Zhao, W.X., Wang, J.: Pdformer: Propagation delay-aware dynamic long-range transformer for traffic flow prediction. In: AAAI, pp. 4365\u20134373 (2023)","DOI":"10.1609\/aaai.v37i4.25556"},{"key":"9_CR8","doi-asserted-by":"crossref","unstructured":"Jin, D., Shi, J., Wang, R., Li, Y., Huang, Y., Yang, Y.B.: Trafformer: Unify time and space in traffic prediction. In: AAAI, pp. 8114\u20138122 (2023)","DOI":"10.1609\/aaai.v37i7.25980"},{"key":"9_CR9","doi-asserted-by":"crossref","unstructured":"Kong, W., Guo, Z., Liu, Y.: Spatio-temporal pivotal graph neural networks for traffic flow forecasting. In: AAAI, pp. 8627\u20138635 (2024)","DOI":"10.1609\/aaai.v38i8.28707"},{"key":"9_CR10","unstructured":"Lan, S., Ma, Y., Huang, W., Wang, W., Yang, H., Li, P.: Dstagnn: Dynamic spatial-temporal aware graph neural network for traffic flow forecasting. In: ICML, pp. 11906\u201311917 (2022)"},{"key":"9_CR11","doi-asserted-by":"publisher","unstructured":"Laub, P.J., Taimre, T., Pollett, P.K.: Hawkes Processes. arXiv e-prints p. arXiv:1507.02822 (2015). https:\/\/doi.org\/10.48550\/arXiv.1507.02822","DOI":"10.48550\/arXiv.1507.02822"},{"key":"9_CR12","doi-asserted-by":"crossref","unstructured":"Li, M., Zhu, Z.: Spatial-temporal fusion graph neural networks for traffic flow forecasting. In: AAAI, pp. 4189\u20134196 (2021)","DOI":"10.1609\/aaai.v35i5.16542"},{"key":"9_CR13","unstructured":"Li, Y., Yu, R., Shahabi, C., Liu, Y.: Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. In: ICLR (2018)"},{"key":"9_CR14","doi-asserted-by":"crossref","unstructured":"Liu, H., Dong, Z., Jiang, R., Deng, J., Deng, J., Chen, Q., Song, X.: Spatio-temporal adaptive embedding makes vanilla transformer sota for traffic forecasting. In: CIKM, pp. 4125\u20134129 (2023)","DOI":"10.1145\/3583780.3615160"},{"key":"9_CR15","doi-asserted-by":"crossref","unstructured":"Seo, Y., Defferrard, M., Vandergheynst, P., Bresson, X.: Structured sequence modeling with graph convolutional recurrent networks. In: Neurips, pp. 362\u2013373 (2018)","DOI":"10.1007\/978-3-030-04167-0_33"},{"key":"9_CR16","unstructured":"Shang, C., Chen, J., Bi, J.: Discrete graph structure learning for forecasting multiple time series. In: ICLR (2021)"},{"key":"9_CR17","doi-asserted-by":"crossref","unstructured":"Shao, Z., Zhang, Z., Wang, F., Wei, W., Xu, Y.: Spatial-temporal identity: A simple yet effective baseline for multivariate time series forecasting. In: CIKM, pp. 4454\u20134458 (2022)","DOI":"10.1145\/3511808.3557702"},{"key":"9_CR18","doi-asserted-by":"crossref","unstructured":"Song, C., Lin, Y., Guo, S., Wan, H.: Spatial-temporal synchronous graph convolutional networks: A new framework for spatial-temporal network data forecasting. In: AAAI, pp. 914\u2013921 (2020)","DOI":"10.1609\/aaai.v34i01.5438"},{"key":"9_CR19","doi-asserted-by":"crossref","unstructured":"Su, H., Wang, X., Chen, Q., Qin, Y.: Efficient adaptive spatial-temporal attention network for traffic flow forecasting. In: ECML-PKDD, pp. 205\u2013220 (2023)","DOI":"10.1007\/978-3-031-43424-2_13"},{"key":"9_CR20","doi-asserted-by":"crossref","unstructured":"Sun, K., Liu, P., Li, P., Liao, Z.: Modwavemlp: Mlp-based mode decomposition and wavelet denoising model to defeat complex structures in traffic forecasting. In: AAAI, pp. 9035\u20139043 (2024)","DOI":"10.1609\/aaai.v38i8.28753"},{"key":"9_CR21","unstructured":"Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Neurips. pp. 3104\u20133112 (2014)"},{"key":"9_CR22","doi-asserted-by":"crossref","unstructured":"Wang, X., et al.: Traffic flow prediction via spatial temporal graph neural network. In: WWW, pp. 1082\u20131092 (2020)","DOI":"10.1145\/3366423.3380186"},{"key":"9_CR23","doi-asserted-by":"crossref","unstructured":"Wu, Z., Pan, S., Long, G., Jiang, J., Chang, X., Zhang, C.: Connecting the dots: Multivariate time series forecasting with graph neural networks. In: SIGKDD, pp. 753\u2013763 (2020)","DOI":"10.1145\/3394486.3403118"},{"key":"9_CR24","doi-asserted-by":"crossref","unstructured":"Wu, Z., Pan, S., Long, G., Jiang, J., Zhang, C.: Graph wavenet for deep spatial-temporal graph modeling. In: IJCAI, pp. 1907\u20131913 (2019)","DOI":"10.24963\/ijcai.2019\/264"},{"key":"9_CR25","doi-asserted-by":"crossref","unstructured":"Yu, B., Yin, H., Zhu, Z.: Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. In: IJCAI, pp. 3634\u20133640 (2018)","DOI":"10.24963\/ijcai.2018\/505"},{"key":"9_CR26","doi-asserted-by":"crossref","unstructured":"Zheng, C., Fan, X., Wang, C., Qi, J.: Gman: A graph multi-attention network for traffic prediction. In: AAAI, pp. 1234\u20131241 (2020)","DOI":"10.1609\/aaai.v34i01.5477"},{"key":"9_CR27","doi-asserted-by":"crossref","unstructured":"Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. In: AAAI, pp. 11106\u201311115 (2021)","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"9_CR28","unstructured":"Zivot, E., Wang, J.: Vector autoregressive models for multivariate time series. Modeling Financial Time Series with S-Plus\u00ae, pp. 385\u2013429 (2006)"}],"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\/978-981-95-3827-0_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T13:03:18Z","timestamp":1781874198000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-3827-0_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819538263","9789819538270"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-3827-0_9","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":"20 June 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"}}]}}