{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T13:56:54Z","timestamp":1726235814064},"publisher-location":"Cham","reference-count":34,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031466601"},{"type":"electronic","value":"9783031466618"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-46661-8_44","type":"book-chapter","created":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T09:02:29Z","timestamp":1699088549000},"page":"661-676","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Graph Convolution Recurrent Denoising Diffusion Model for\u00a0Multivariate Probabilistic Temporal Forecasting"],"prefix":"10.1007","author":[{"given":"Ruikun","family":"Li","sequence":"first","affiliation":[]},{"given":"Xuliang","family":"Li","sequence":"additional","affiliation":[]},{"given":"Shiying","family":"Gao","sequence":"additional","affiliation":[]},{"given":"S. T. Boris","family":"Choy","sequence":"additional","affiliation":[]},{"given":"Junbin","family":"Gao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,5]]},"reference":[{"key":"44_CR1","doi-asserted-by":"crossref","unstructured":"Afifi, H., Elmahdy, M., El Saban, M., Abu-Elkheir, M.: Probabilistic time series forecasting for unconventional oil and gas producing wells. In: The 2nd Novel Intelligent and Leading Emerging Sciences Conference, pp. 450\u2013455 (2020)","DOI":"10.1109\/NILES50944.2020.9257962"},{"key":"44_CR2","unstructured":"an den Oord, A., Kalchbrenner, N., Espeholt, L., kavukcuoglu, K., Vinyals, O., Graves, A.: Conditional image generation with pixel CNN decoders. In: Advances in Neural Information Processing Systems. vol. 29, pp. 4790\u20134798 (2016)"},{"key":"44_CR3","doi-asserted-by":"crossref","unstructured":"Bai, J., et al.: A3t-gcn: attention temporal graph convolutional network for traffic forecasting. ISPRS Int. J. Geo Inf. 10(7), 485 (2021)","DOI":"10.3390\/ijgi10070485"},{"key":"44_CR4","doi-asserted-by":"crossref","unstructured":"Chen, H., Rossi, R.A., Mahadik, K., Kim, S., Eldardiry, H.: Graph deep factors for probabilistic time-series forecasting. ACM Trans. Knowl. Discov. Data (2022)","DOI":"10.1145\/3543511"},{"key":"44_CR5","unstructured":"Chung, F.R.: Spectral Graph Theory, vol. 92. American Mathematical Soc. (1997)"},{"key":"44_CR6","unstructured":"Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: Neural Information Processing Systems 2014 Workshop on Deep Learning (2014)"},{"key":"44_CR7","unstructured":"Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: Advances in Neural Information Processing Systems, vol. 29 (2016)"},{"key":"44_CR8","unstructured":"Gasthaus, J., et al.: Probabilistic forecasting with spline quantile function RNNs. In: The 22nd International Conference on Artificial Intelligence and Statistics, pp. 1901\u20131910 (2019)"},{"key":"44_CR9","unstructured":"Graves, A.: Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850 (2013)"},{"issue":"4","key":"44_CR10","first-page":"4030","volume":"36","author":"H He","year":"2022","unstructured":"He, H., Zhang, Q., Bai, S., Yi, K., Niu, Z.: CATN: cross attentive tree-aware network for multivariate time series forecasting. Proc. AAAI Conf. Artif. Intell. 36(4), 4030\u20134038 (2022)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"44_CR11","doi-asserted-by":"crossref","unstructured":"Hmamouche, Y., Przymus, P.M., Alouaoui, H., Casali, A., Lakhal, L.: Large multivariate time series forecasting: survey on methods and scalability. In: Utilizing Big Data Paradigms for Business Intelligence, pp. 170\u2013197. IGI Global (2019)","DOI":"10.4018\/978-1-5225-4963-5.ch006"},{"key":"44_CR12","first-page":"6840","volume":"33","author":"J Ho","year":"2020","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Adv. Neural. Inf. Process. Syst. 33, 6840\u20136851 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"44_CR13","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (2015)"},{"key":"44_CR14","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations (2016)"},{"key":"44_CR15","unstructured":"Kong, Z., Ping, W., Huang, J., Zhao, K., Catanzaro, B.: Diffwave: a versatile diffusion model for audio synthesis. In: International Conference on Learning Representations (2021)"},{"key":"44_CR16","doi-asserted-by":"crossref","unstructured":"Lai, G., Chang, W.C., Yang, Y., Liu, H.: Modeling long-and short-term temporal patterns with deep neural networks. In: The 41st International Conference on Research & Development in Information Retrieval, pp. 95\u2013104 (2018)","DOI":"10.1145\/3209978.3210006"},{"key":"44_CR17","unstructured":"Li, Y., Yu, R., Shahabi, C., Liu, Y.: Diffusion convolutional recurrent neural network: data-driven traffic forecasting. In: International Conference on Learning Representations (2017)"},{"issue":"2194","key":"44_CR18","doi-asserted-by":"publisher","first-page":"20200209","DOI":"10.1098\/rsta.2020.0209","volume":"379","author":"B Lim","year":"2021","unstructured":"Lim, B., Zohren, S.: Time-series forecasting with deep learning: a survey. Phil. Trans. R. Soc. A 379(2194), 20200209 (2021)","journal-title":"Phil. Trans. R. Soc. A"},{"key":"44_CR19","doi-asserted-by":"publisher","first-page":"899","DOI":"10.1016\/B978-0-444-62731-5.00016-6","volume":"2","author":"A Patton","year":"2013","unstructured":"Patton, A.: Copula methods for forecasting multivariate time series. Handb. Econ. Forecast. 2, 899\u2013960 (2013)","journal-title":"Handb. Econ. Forecast."},{"key":"44_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: Advances in Neural Information Processing Systems, vol. 31 (2018)"},{"key":"44_CR21","unstructured":"Rasul, K., Seward, C., Schuster, I., Vollgraf, R.: Autoregressive denoising diffusion models for multivariate probabilistic time series forecasting. In: International Conference on Machine Learning, pp. 8857\u20138868 (2021)"},{"key":"44_CR22","doi-asserted-by":"crossref","unstructured":"Rozemberczki, B., et al.: PyTorch geometric temporal: spatiotemporal signal processing with neural machine learning models. In: Proceedings of the 30th International Conference on Information and Knowledge Management, pp. 4564\u20134573 (2021)","DOI":"10.1145\/3459637.3482014"},{"key":"44_CR23","doi-asserted-by":"publisher","unstructured":"Seo, Y., Defferrard, M., Vandergheynst, P., Bresson, X.: Structured sequence modeling with graph convolutional recurrent networks. In: Cheng, L., Leung, A.C.S., Ozawa, S. (eds.) ICONIP 2018. LNCS, vol. 11301, pp. 362\u2013373. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-04167-0_33","DOI":"10.1007\/978-3-030-04167-0_33"},{"key":"44_CR24","unstructured":"Shang, C., Chen, J., Bi, J.: Discrete graph structure learning for forecasting multiple time series. arXiv preprint arXiv:2101.06861 (2021)"},{"issue":"8","key":"44_CR25","doi-asserted-by":"publisher","first-page":"1421","DOI":"10.1007\/s10994-019-05815-0","volume":"108","author":"SY Shih","year":"2019","unstructured":"Shih, S.Y., Sun, F.K., Lee, H.Y.: Temporal pattern attention for multivariate time series forecasting. Mach. Learn. 108(8), 1421\u20131441 (2019)","journal-title":"Mach. Learn."},{"issue":"3","key":"44_CR26","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1109\/MSP.2012.2235192","volume":"30","author":"DI Shuman","year":"2013","unstructured":"Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: extending high-dimensional data analysis to networks and other irregular domains. IEEE Signal Process. Mag. 30(3), 83\u201398 (2013)","journal-title":"IEEE Signal Process. Mag."},{"key":"44_CR27","first-page":"12438","volume":"33","author":"Y Song","year":"2020","unstructured":"Song, Y., Ermon, S.: Improved techniques for training score-based generative models. Adv. Neural. Inf. Process. Syst. 33, 12438\u201312448 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"44_CR28","unstructured":"Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, vol. 27 (2014)"},{"key":"44_CR29","first-page":"24804","volume":"34","author":"Y Tashiro","year":"2021","unstructured":"Tashiro, Y., Song, J., Song, Y., Ermon, S.: CSDI: conditional score-based diffusion models for probabilistic time series imputation. Adv. Neural. Inf. Process. Syst. 34, 24804\u201324816 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"44_CR30","doi-asserted-by":"crossref","unstructured":"Wu, Z., Pan, S., Long, G., Jiang, J., Zhang, C.: Graph wavenet for deep spatial-temporal graph modeling. In: The 28th International Joint Conference on Artificial Intelligence (IJCAI). International Joint Conferences on Artificial Intelligence Organization (2019)","DOI":"10.24963\/ijcai.2019\/264"},{"issue":"1","key":"44_CR31","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","volume":"32","author":"Z Wu","year":"2020","unstructured":"Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE Trans. Neural Netw. Learn. Syst. 32(1), 4\u201324 (2020)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"44_CR32","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: The 26th ACM International Conference on Knowledge Discovery & Data Mining, pp. 753\u2013763 (2020)","DOI":"10.1145\/3394486.3403118"},{"key":"44_CR33","unstructured":"Yang, L., et al.: Diffusion models: a comprehensive survey of methods and applications. arXiv preprint arXiv:2209.00796 (2022)"},{"key":"44_CR34","doi-asserted-by":"crossref","unstructured":"Zhao, L., et al.: T-GCN: a temporal graph convolutional network for traffic prediction. IEEE Trans. Intell. Transp. Syst. 21(9), 3848\u20133858 (2019)","DOI":"10.1109\/TITS.2019.2935152"}],"container-title":["Lecture Notes in Computer Science","Advanced Data Mining and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-46661-8_44","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T09:07:58Z","timestamp":1699088878000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-46661-8_44"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031466601","9783031466618"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-46661-8_44","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"5 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ADMA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Data Mining and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenyang","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 August 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adma2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/adma2023.uqcloud.net\/","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":"Yes. Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"503","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":"216","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":"43% - 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":"2.97","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":"3.77","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}