{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T00:48:40Z","timestamp":1782434920680,"version":"3.54.5"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030865139","type":"print"},{"value":"9783030865146","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-86514-6_12","type":"book-chapter","created":{"date-parts":[[2021,9,9]],"date-time":"2021-09-09T12:05:38Z","timestamp":1631189138000},"page":"187-203","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Spatio-Temporal Multi-graph Networks for Demand Forecasting in Online Marketplaces"],"prefix":"10.1007","author":[{"given":"Ankit","family":"Gandhi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"family":"Aakanksha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sivaramakrishnan","family":"Kaveri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vineet","family":"Chaoji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,9,10]]},"reference":[{"key":"12_CR1","unstructured":"Bai, S., Kolter, J.Z., Koltun, V.: An empirical evaluation of generic convolutional and recurrent networks for sequence modeling (2018)"},{"key":"12_CR2","doi-asserted-by":"crossref","unstructured":"Bandara, K., et al.: Sales demand forecast in e-commerce using a long short-term memory neural network methodology. In: Neural Information Processing (2019)","DOI":"10.1007\/978-3-030-36718-3_39"},{"key":"12_CR3","unstructured":"Benidis, K., Rangapuram, S.S., Flunkert, V., et. al.: Neural forecasting: introduction and literature overview (2020)"},{"key":"12_CR4","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1111\/j.2517-6161.1964.tb00553.x","volume":"26","author":"GEP Box","year":"1964","unstructured":"Box, G.E.P., Cox, D.R.: An analysis of transformations. J. Roy. Stat. Soc.: Ser. B (Methodol.) 26, 211\u2013243 (1964)","journal-title":"J. Roy. Stat. Soc.: Ser. B (Methodol.)"},{"key":"12_CR5","volume-title":"Time Series Analysis: Forecasting and Control","author":"GEP Box","year":"2015","unstructured":"Box, G.E.P., Jenkins, G.M., Reinsel, G.C., Ljung, G.M.: Time Series Analysis: Forecasting and Control. Wiley, New York (2015)"},{"key":"12_CR6","doi-asserted-by":"crossref","unstructured":"Bronstein, M.M., Bruna, J., LeCun, Y., Szlam, A., Vandergheynst, P.: Geometric deep learning: going beyond Euclidean data. CoRR (2016)","DOI":"10.1109\/MSP.2017.2693418"},{"key":"12_CR7","unstructured":"Cao, D., Wang, Y., Duan, J., Zhang, C., Zhu, X., et al.: Spectral temporal graph neural network for multivariate time-series forecasting. In: NeurIPS (2020)"},{"key":"12_CR8","doi-asserted-by":"crossref","unstructured":"Chen, Y., Kang, Y., Chen, Y., Wang, Z.: Probabilistic forecasting with temporal convolutional neural network (2020)","DOI":"10.1016\/j.neucom.2020.03.011"},{"key":"12_CR9","doi-asserted-by":"publisher","DOI":"10.1093\/acprof:oso\/9780199641178.001.0001","volume-title":"Time Series Analysis by State Space Methods","author":"J Durbin","year":"2012","unstructured":"Durbin, J., Koopman, S.J.: Time Series Analysis by State Space Methods, 2nd edn. Oxford University Press, Oxford (2012)","edition":"2"},{"key":"12_CR10","unstructured":"Ghorbani, M., Baghshah, M.S., Rabiee, H.R.: Multi-layered graph embedding with graph convolutional networks. CoRR (2018)"},{"key":"12_CR11","unstructured":"Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. CoRR (2017)"},{"key":"12_CR12","unstructured":"Hamilton, W.L., Ying, R., Leskovec, J.: Representation learning on graphs: methods and applications. CoRR (2017)"},{"key":"12_CR13","series-title":"Springer Series in Statistics","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-71918-2","volume-title":"Forecasting with Exponential Smoothing: The State Space Approach","author":"R Hyndman","year":"2008","unstructured":"Hyndman, R., Koehler, A.B., Ord, J.K., Snyder, R.D.: Forecasting with Exponential Smoothing: The State Space Approach. Springer Series in Statistics, Springer, Heidelberg (2008). https:\/\/doi.org\/10.1007\/978-3-540-71918-2"},{"key":"12_CR14","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. CoRR (2016)"},{"key":"12_CR15","doi-asserted-by":"crossref","unstructured":"Lai, G., Chang, W., Yang, Y., Liu, H.: Modeling long- and short-term temporal patterns with deep neural networks. CoRR (2017)","DOI":"10.1145\/3209978.3210006"},{"key":"12_CR16","unstructured":"Li, Y., Yu, R., Shahabi, C., Liu, Y.: Diffusion convolutional recurrent neural network: data-driven traffic forecasting. In: ICLR (2018)"},{"key":"12_CR17","doi-asserted-by":"crossref","unstructured":"McAuley, J., Pandey, R., Leskovec, J.: Inferring networks of substitutable and complementary products. In: KDD (2015)","DOI":"10.1145\/2783258.2783381"},{"key":"12_CR18","unstructured":"Monti, F., Bronstein, M.M., Bresson, X.: Geometric matrix completion with recurrent multi-graph neural networks. CoRR (2017)"},{"key":"12_CR19","unstructured":"Mukherjee, S., Shankar, D., Ghosh, A., et al.: ARMDN: associative and recurrent mixture density networks for eRetail demand forecasting. CoRR (2018)"},{"key":"12_CR20","unstructured":"Rangapuram, S.S., Seeger, M.W., Gasthaus, J., Stella, L., Wang, Y., Januschowski, T.: Deep state space models for time series forecasting. In: NeurIPS (2018)"},{"key":"12_CR21","doi-asserted-by":"crossref","unstructured":"Salinas, D., Flunkert, V., Gasthaus, J.: DeepAR: probabilistic forecasting with autoregressive recurrent networks (2019)","DOI":"10.1016\/j.ijforecast.2019.07.001"},{"key":"12_CR22","unstructured":"Sen, R., Yu, H.F., Dhillon, I.S.: Think globally, act locally: a deep neural network approach to high-dimensional time series forecasting. In: NeurIPS (2019)"},{"key":"12_CR23","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., Bengio, Y.: Graph attention networks. In: ICLR (2018)"},{"key":"12_CR24","doi-asserted-by":"crossref","unstructured":"Wang, Z., Jiang, Z., Ren, Z., et al.: A path-constrained framework for discriminating substitutable and complementary products in e-commerce. In: WSDM (2018)","DOI":"10.1145\/3159652.3159710"},{"key":"12_CR25","unstructured":"Wen, R., Torkkola, K., Narayanaswamy, B., Madeka, D.: A multi-horizon quantile recurrent forecaster (2018)"},{"key":"12_CR26","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-2019 (2019)","DOI":"10.24963\/ijcai.2019\/264"},{"key":"12_CR27","doi-asserted-by":"crossref","unstructured":"Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W.L., Leskovec, J.: Graph convolutional neural networks for web-scale recommender systems. CoRR (2018)","DOI":"10.1145\/3219819.3219890"},{"key":"12_CR28","unstructured":"You, J., Ying, R., Ren, X., Hamilton, W.L., Leskovec, J.: GraphRNN: a deep generative model for graphs. CoRR (2018)"},{"key":"12_CR29","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 (2018)","DOI":"10.24963\/ijcai.2018\/505"},{"key":"12_CR30","doi-asserted-by":"crossref","unstructured":"Zitnik, M., Agrawal, M., Leskovec, J.: Modeling polypharmacy side effects with graph convolutional networks. CoRR (2018)","DOI":"10.1101\/258814"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-86514-6_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T22:04:08Z","timestamp":1757369048000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-86514-6_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030865139","9783030865146"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-86514-6_12","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"10 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bilbao","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 September 2021","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":"ecml2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2021.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"869","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"210","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3-4","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3-9","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held online due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}