{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:52:32Z","timestamp":1742914352040,"version":"3.40.3"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031781940"},{"type":"electronic","value":"9783031781957"}],"license":[{"start":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T00:00:00Z","timestamp":1733184000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T00:00:00Z","timestamp":1733184000000},"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\/978-3-031-78195-7_4","type":"book-chapter","created":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T11:10:29Z","timestamp":1733137829000},"page":"46-61","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SSMT: Few-Shot Traffic Forecasting with Single Source Meta-transfer"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-8088-2492","authenticated-orcid":false,"given":"Kishor Kumar","family":"Bhaumik","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3224-0610","authenticated-orcid":false,"given":"Minha","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9466-1458","authenticated-orcid":false,"given":"Fahim Faisal","family":"Niloy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amin Ahsan","family":"Ali","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8983-1542","authenticated-orcid":false,"given":"Simon S.","family":"Woo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,3]]},"reference":[{"key":"4_CR1","unstructured":"M.\u00a0S. Ahmed and A.\u00a0R. Cook. Analysis of freeway traffic time-series data by using Box-Jenkins techniques. Number 722. 1979"},{"key":"4_CR2","doi-asserted-by":"crossref","unstructured":"L.\u00a0Bai, L.\u00a0Yao, S.\u00a0Kanhere, X.\u00a0Wang, Q.\u00a0Sheng, et\u00a0al. Stg2seq: Spatial-temporal graph to sequence model for multi-step passenger demand forecasting. arXiv preprint arXiv:1905.10069, 2019","DOI":"10.24963\/ijcai.2019\/274"},{"key":"4_CR3","unstructured":"A.\u00a0Cini, I.\u00a0Marisca, D.\u00a0Zambon, and C.\u00a0Alippi. Taming local effects in graph-based spatiotemporal forecasting. Advances in Neural Information Processing Systems, 36, 2024"},{"key":"4_CR4","unstructured":"Didi-Chuxing. Didi chuxing gaia initiative., 2020. Accessed: 2020-02-14"},{"issue":"9","key":"4_CR5","doi-asserted-by":"publisher","first-page":"8943","DOI":"10.1109\/JIOT.2020.2999446","volume":"7","author":"TH Do","year":"2020","unstructured":"Do, T.H., Tsiligianni, E., Qin, X., Hofman, J., La Manna, V.P., Philips, W., Deligiannis, N.: Graph-deep-learning-based inference of fine-grained air quality from mobile iot sensors. IEEE Internet Things J. 7(9), 8943\u20138955 (2020)","journal-title":"IEEE Internet Things J."},{"key":"4_CR6","doi-asserted-by":"crossref","unstructured":"Y.\u00a0Du, J.\u00a0Wang, W.\u00a0Feng, S.\u00a0Pan, T.\u00a0Qin, R.\u00a0Xu, and C.\u00a0Wang. Adarnn: Adaptive learning and forecasting of time series. In Proceedings of the 30th ACM international conference on information & knowledge management, pages 402\u2013411, 2021","DOI":"10.1145\/3459637.3482315"},{"key":"4_CR7","unstructured":"C.\u00a0Finn, P.\u00a0Abbeel, and S.\u00a0Levine. Model-agnostic meta-learning for fast adaptation of deep networks. In International conference on machine learning, pages 1126\u20131135. PMLR, 2017"},{"key":"4_CR8","doi-asserted-by":"crossref","unstructured":"D.\u00a0Gong, L.\u00a0Liu, V.\u00a0Le, B.\u00a0Saha, M.\u00a0R. Mansour, S.\u00a0Venkatesh, and A.\u00a0v.\u00a0d. Hengel. Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection. In Proceedings of the IEEE\/CVF International Conference on Computer Vision, pages 1705\u20131714, 2019","DOI":"10.1109\/ICCV.2019.00179"},{"issue":"11","key":"4_CR9","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., Li, X., Cong, G.: 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":"4_CR10","unstructured":"E.\u00a0Jang, S.\u00a0Gu, and B.\u00a0Poole. Categorical reparameterization with gumbel-softmax. arXiv preprint arXiv:1611.01144, 2016"},{"key":"4_CR11","doi-asserted-by":"publisher","first-page":"8078","DOI":"10.1609\/aaai.v37i7.25976","volume":"37","author":"R Jiang","year":"2023","unstructured":"Jiang, R., Wang, Z., Yong, J., Jeph, P., Chen, Q., Kobayashi, Y., Song, X., Fukushima, S., Suzumura, T.: Spatio-temporal meta-graph learning for traffic forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence 37, 8078\u20138086 (2023)","journal-title":"In Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"4_CR12","doi-asserted-by":"crossref","unstructured":"Y.\u00a0Jin, K.\u00a0Chen, and Q.\u00a0Yang. Selective cross-city transfer learning for traffic prediction via source city region re-weighting. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 731\u2013741, 2022","DOI":"10.1145\/3534678.3539250"},{"key":"4_CR13","doi-asserted-by":"crossref","unstructured":"A.\u00a0J.\u00a0R. Kumar and B.\u00a0Bhanu. Micro-expression classification based on landmark relations with graph attention convolutional network. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pages 1511\u20131520, 2021","DOI":"10.1109\/CVPRW53098.2021.00167"},{"key":"4_CR14","unstructured":"Y.\u00a0Li, R.\u00a0Yu, C.\u00a0Shahabi, and Y.\u00a0Liu. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv preprint arXiv:1707.01926, 2017"},{"key":"4_CR15","doi-asserted-by":"crossref","unstructured":"Z.\u00a0Liu, G.\u00a0Zheng, and Y.\u00a0Yu. Cross-city few-shot traffic forecasting via traffic pattern bank. arXiv preprint arXiv:2308.09727, 2023","DOI":"10.1145\/3583780.3614829"},{"key":"4_CR16","doi-asserted-by":"crossref","unstructured":"B.\u00a0Lu, X.\u00a0Gan, H.\u00a0Jin, L.\u00a0Fu, and H.\u00a0Zhang. Spatiotemporal adaptive gated graph convolution network for urban traffic flow forecasting. In Proceedings of the 29th ACM International conference on information & knowledge management, pages 1025\u20131034, 2020","DOI":"10.1145\/3340531.3411894"},{"key":"4_CR17","doi-asserted-by":"crossref","unstructured":"B.\u00a0Lu, X.\u00a0Gan, W.\u00a0Zhang, H.\u00a0Yao, L.\u00a0Fu, and X.\u00a0Wang. Spatio-temporal graph few-shot learning with cross-city knowledge transfer. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 1162\u20131172, 2022","DOI":"10.1145\/3534678.3539281"},{"key":"4_CR18","doi-asserted-by":"crossref","unstructured":"Z.\u00a0Pan, Y.\u00a0Liang, W.\u00a0Wang, Y.\u00a0Yu, Y.\u00a0Zheng, and J.\u00a0Zhang. Urban traffic prediction from spatio-temporal data using deep meta learning. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pages 1720\u20131730, 2019","DOI":"10.1145\/3292500.3330884"},{"key":"4_CR19","doi-asserted-by":"crossref","unstructured":"H.\u00a0Park, J.\u00a0Noh, and B.\u00a0Ham. Learning memory-guided normality for anomaly detection. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pages 14372\u201314381, 2020","DOI":"10.1109\/CVPR42600.2020.01438"},{"key":"4_CR20","doi-asserted-by":"crossref","unstructured":"H.\u00a0Park, J.\u00a0Noh, and B.\u00a0Ham. Learning memory-guided normality for anomaly detection. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pages 14372\u201314381, 2020","DOI":"10.1109\/CVPR42600.2020.01438"},{"key":"4_CR21","unstructured":"A.\u00a0Santoro, S.\u00a0Bartunov, M.\u00a0Botvinick, D.\u00a0Wierstra, and T.\u00a0Lillicrap. Meta-learning with memory-augmented neural networks. In International conference on machine learning, pages 1842\u20131850. PMLR, 2016"},{"key":"4_CR22","unstructured":"A.\u00a0Vaswani, N.\u00a0Shazeer, N.\u00a0Parmar, J.\u00a0Uszkoreit, L.\u00a0Jones, A.\u00a0N. Gomez, \u0141.\u00a0Kaiser, and I.\u00a0Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017"},{"key":"4_CR23","unstructured":"O.\u00a0Vinyals, C.\u00a0Blundell, T.\u00a0Lillicrap, D.\u00a0Wierstra, et\u00a0al. Matching networks for one shot learning. Advances in neural information processing systems, 29, 2016"},{"key":"4_CR24","doi-asserted-by":"crossref","unstructured":"L.\u00a0Wang, X.\u00a0Geng, X.\u00a0Ma, F.\u00a0Liu, and Q.\u00a0Yang. Cross-city transfer learning for deep spatio-temporal prediction. arXiv preprint arXiv:1802.00386, 2018","DOI":"10.24963\/ijcai.2019\/262"},{"key":"4_CR25","doi-asserted-by":"crossref","unstructured":"Z.\u00a0Wu, S.\u00a0Pan, G.\u00a0Long, J.\u00a0Jiang, and C.\u00a0Zhang. Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121, 2019","DOI":"10.24963\/ijcai.2019\/264"},{"key":"4_CR26","doi-asserted-by":"crossref","unstructured":"H.\u00a0Yao, Y.\u00a0Liu, Y.\u00a0Wei, X.\u00a0Tang, and Z.\u00a0Li. Learning from multiple cities: A meta-learning approach for spatial-temporal prediction. In The World Wide Web Conference, pages 2181\u20132191, 2019","DOI":"10.1145\/3308558.3313577"},{"key":"4_CR27","doi-asserted-by":"crossref","unstructured":"B.\u00a0Yu, H.\u00a0Yin, and Z.\u00a0Zhu. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875, 2017","DOI":"10.24963\/ijcai.2018\/505"},{"key":"4_CR28","doi-asserted-by":"crossref","unstructured":"H.\u00a0Yu, T.\u00a0Li, W.\u00a0Yu, J.\u00a0Li, Y.\u00a0Huang, L.\u00a0Wang, and A.\u00a0Liu. Regularized graph structure learning with semantic knowledge for multi-variates time-series forecasting. arXiv preprint arXiv:2210.06126, 2022","DOI":"10.24963\/ijcai.2022\/328"},{"key":"4_CR29","unstructured":"Y.\u00a0Yuan, C.\u00a0Shao, J.\u00a0Ding, D.\u00a0Jin, and Y.\u00a0Li. A generative pre-training framework for spatio-temporal graph transfer learning. arXiv preprint arXiv:2402.11922, 2024"},{"issue":"2","key":"4_CR30","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1109\/JAS.2022.106004","volume":"10","author":"W Zhang","year":"2022","unstructured":"Zhang, W., Deng, L., Zhang, L., Wu, D.: A survey on negative transfer. IEEE\/CAA Journal of Automatica Sinica 10(2), 305\u2013329 (2022)","journal-title":"IEEE\/CAA Journal of Automatica Sinica"},{"key":"4_CR31","doi-asserted-by":"crossref","unstructured":"Y.\u00a0Zhang, Y.\u00a0Li, X.\u00a0Zhou, X.\u00a0Kong, and J.\u00a0Luo. Strans-gan: Spatially-transferable generative adversarial networks for urban traffic estimation. In 2022 IEEE International Conference on Data Mining (ICDM), pages 743\u2013752. IEEE, 2022","DOI":"10.1109\/ICDM54844.2022.00085"},{"issue":"9","key":"4_CR32","doi-asserted-by":"publisher","first-page":"3848","DOI":"10.1109\/TITS.2019.2935152","volume":"21","author":"L Zhao","year":"2019","unstructured":"Zhao, L., Song, Y., Zhang, C., Liu, Y., Wang, P., Lin, T., Deng, M., Li, H.: T-gcn: A temporal graph convolutional network for traffic prediction. IEEE Trans. Intell. Transp. Syst. 21(9), 3848\u20133858 (2019)","journal-title":"IEEE Trans. Intell. Transp. Syst."}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-78195-7_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T12:03:34Z","timestamp":1733141014000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-78195-7_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,3]]},"ISBN":["9783031781940","9783031781957"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-78195-7_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,12,3]]},"assertion":[{"value":"3 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kolkata","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpr2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icpr2024.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}