{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T02:20:24Z","timestamp":1772936424119,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":21,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819564255","type":"print"},{"value":"9789819564262","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\/978-981-95-6426-2_2","type":"book-chapter","created":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T08:05:58Z","timestamp":1772870758000},"page":"15-28","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["PTE-DHNet: Priority-Aware Temporal Embedding and\u00a0Dynamic Hypergraph Network for\u00a0Steel Transportation Volume Prediction"],"prefix":"10.1007","author":[{"given":"Xiaopeng","family":"Huang","sequence":"first","affiliation":[]},{"given":"Yitao","family":"Dong","sequence":"additional","affiliation":[]},{"given":"Jiali","family":"Mao","sequence":"additional","affiliation":[]},{"given":"Jiajun","family":"Liao","sequence":"additional","affiliation":[]},{"given":"Yiming","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Shuaihua","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,4,1]]},"reference":[{"key":"2_CR1","doi-asserted-by":"crossref","unstructured":"Alharthi, M., Mahmood, A.: xLSTMTime: long-term time series forecasting with xLSTM. AI 5(3), 1482\u20131495 (2024)","DOI":"10.3390\/ai5030071"},{"issue":"16","key":"2_CR2","doi-asserted-by":"publisher","first-page":"13253","DOI":"10.1007\/s00521-021-06746-5","volume":"34","author":"J Barzola-Monteses","year":"2022","unstructured":"Barzola-Monteses, J., G\u00f3mez-Romero, J., Espinoza-Andaluz, M., et al.: Hydropower production prediction using artificial neural networks: an ecuadorian application case. Neural Comput. Appl. 34(16), 13253\u201313266 (2022)","journal-title":"Neural Comput. Appl."},{"issue":"9","key":"2_CR3","doi-asserted-by":"publisher","first-page":"6069","DOI":"10.1109\/TII.2020.2967556","volume":"16","author":"A Essien","year":"2020","unstructured":"Essien, A., Giannetti, C.: A deep learning model for smart manufacturing using convolutional LSTM neural network autoencoders. IEEE Trans. Ind. Inf. 16(9), 6069\u20136078 (2020)","journal-title":"IEEE Trans. Ind. Inf."},{"issue":"3","key":"2_CR4","doi-asserted-by":"publisher","first-page":"3181","DOI":"10.1109\/TPAMI.2022.3182052","volume":"45","author":"Y Gao","year":"2023","unstructured":"Gao, Y., Feng, Y., Ji, S., et al.: HGNN+: general hypergraph neural networks. IEEE Trans. Pattern Anal. Mach. Intell. 45(3), 3181\u20133199 (2023)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"11","key":"2_CR5","doi-asserted-by":"publisher","first-page":"5415","DOI":"10.1109\/TKDE.2021.3056502","volume":"34","author":"S Guo","year":"2021","unstructured":"Guo, S., Lin, Y., Wan, H., et al.: Learning dynamics and heterogeneity of spatial-temporal graph data for traffic forecasting. IEEE Trans. Knowl. Data Eng. 34(11), 5415\u20135428 (2021)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"2_CR6","doi-asserted-by":"crossref","unstructured":"Han, Y., Du, Z., Geng, Z., et al.: Production prediction model of complex industrial processes based on GRU neural network, pp. 1102\u20131106 (2020)","DOI":"10.1109\/CAC51589.2020.9327273"},{"key":"2_CR7","doi-asserted-by":"crossref","unstructured":"Jiang, J., Han, C., Zhao, W. X., et al.: PDFormer: propagation delay-aware dynamic long-range transformer for traffic flow prediction. In: AAAI, vol. 37, no. 4, pp. 4365\u20134373 (2023)","DOI":"10.1609\/aaai.v37i4.25556"},{"key":"2_CR8","doi-asserted-by":"crossref","unstructured":"Kong, W., Guo, Z., Liu, Y.: Spatio-temporal pivotal graph neural networks for traffic flow forecasting. In: AAAI, vol. 38, no. 8, pp. 8627\u20138635 (2024)","DOI":"10.1609\/aaai.v38i8.28707"},{"key":"2_CR9","doi-asserted-by":"crossref","unstructured":"Li, M., Zhu, Z.: Spatial-temporal fusion graph neural networks for traffic flow forecasting. In: AAAI, vol. 35, no. 5, pp. 4189\u20134196 (2021)","DOI":"10.1609\/aaai.v35i5.16542"},{"key":"2_CR10","doi-asserted-by":"crossref","unstructured":"Liu, J., Chen, L., Xu, W., et al.: Novel production prediction model of gasoline production processes for energy saving and economic increasing based on AM-GRU integrating the UMAP algorithm. Energy (Oxford) 261, 125536 (2023)","DOI":"10.1016\/j.energy.2022.125536"},{"key":"2_CR11","unstructured":"Liu, Y., Hu, T., Zhang, H., et al.: iTransformer: inverted transformers are effective for time series forecasting. In: ICLR, vol. 12 (2024)"},{"issue":"1","key":"2_CR12","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1145\/2949741.2949758","volume":"45","author":"J Paparrizos","year":"2016","unstructured":"Paparrizos, J., Gravano, L.: k-shape: efficient and accurate clustering of time series. SIGMOD 45(1), 69\u201376 (2016)","journal-title":"SIGMOD"},{"key":"2_CR13","first-page":"2476","volume":"32","author":"Z Peng","year":"2023","unstructured":"Peng, Z., Zongqian, W., Xiangxiang, Z., et al.: Totally dynamic hypergraph neural networks. IJCAI 32, 2476\u20132483 (2023)","journal-title":"IJCAI"},{"key":"2_CR14","unstructured":"Ren, Y., Zhao, Y., Zhang, T., et al.: Pathformer: multi-scale transformers with adaptive pathways for time series forecasting. In: ICLR, vol. 12 (2024)"},{"key":"2_CR15","doi-asserted-by":"crossref","unstructured":"Rong, Y., Yao, J., Liu, J., et al.: GBTTE: graph attention network based bus travel time estimation. In: CIKM, vol. 32, pp. 4794\u20134800 (2023)","DOI":"10.1145\/3583780.3614730"},{"key":"2_CR16","doi-asserted-by":"crossref","unstructured":"Wang, J., Yang, H., Shan, J., Song, X., Jiang, J. Attention-based spatial-temporal fusion networks for traffic flow prediction. In: WISA, pp. 500\u2013511 (2024)","DOI":"10.1007\/978-981-97-7707-5_41"},{"key":"2_CR17","unstructured":"Wu, D., Hu, J. Y.-C., Li, W., et al.: STanHop: sparse tandem hopfield model for memory-enhanced time series prediction. In: ICLR, vol. 12 (2024)"},{"key":"2_CR18","first-page":"1907","volume":"28","author":"Z Wu","year":"2019","unstructured":"Wu, Z., Pan, S., Long, G., et al.: Graph WaveNet for deep spatial-temporal graph modeling. IJCAI 28, 1907\u20131913 (2019)","journal-title":"IJCAI"},{"key":"2_CR19","first-page":"3634","volume":"27","author":"T Yu","year":"2018","unstructured":"Yu, T., Yin, H., Zhu, Z.: Spatio-temporal graph convolutional neural network: a deep learning framework for traffic forecasting. IJCAI 27, 3634\u20133640 (2018)","journal-title":"IJCAI"},{"key":"2_CR20","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Luo, X., Ju, W.: Dynamic hypergraph structure learning for traffic flow forecasting. In: ICDE, vol. 39, pp. 2303\u20132316 (2023)","DOI":"10.1109\/ICDE55515.2023.00178"},{"key":"2_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: AAAI, vol. 35, no. 12, pp. 11106\u201311115 (2021)","DOI":"10.1609\/aaai.v35i12.17325"}],"container-title":["Lecture Notes in Computer Science","Web Information Systems and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-6426-2_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T08:06:02Z","timestamp":1772870762000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-6426-2_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819564255","9789819564262"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-6426-2_2","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":"1 April 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"WISA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Web Information Systems and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xi'an","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":"1 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2025","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":"wisa22025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.ccf.org.cn\/Chapters\/TC\/TC_Listing\/TCIS_\/tzzw\/2025-02-21\/838214.shtml","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}