{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T04:14:49Z","timestamp":1769746489874,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":21,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T00:00:00Z","timestamp":1697846400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,10,21]]},"DOI":"10.1145\/3583780.3615487","type":"proceedings-article","created":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T07:45:42Z","timestamp":1697874342000},"page":"4523-4529","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["Monotonic Neural Ordinary Differential Equation: Time-series Forecasting for Cumulative Data"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5785-0741","authenticated-orcid":false,"given":"Zhichao","family":"Chen","sequence":"first","affiliation":[{"name":"Ant Group, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0641-3423","authenticated-orcid":false,"given":"Leilei","family":"Ding","sequence":"additional","affiliation":[{"name":"Ant Group, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6075-1816","authenticated-orcid":false,"given":"Zhixuan","family":"Chu","sequence":"additional","affiliation":[{"name":"Ant Group, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4542-5427","authenticated-orcid":false,"given":"Yucheng","family":"Qi","sequence":"additional","affiliation":[{"name":"Ant Group, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1861-7442","authenticated-orcid":false,"given":"Jianmin","family":"Huang","sequence":"additional","affiliation":[{"name":"Ant Group, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3243-487X","authenticated-orcid":false,"given":"Hao","family":"Wang","sequence":"additional","affiliation":[{"name":"Ant Group, Hangzhou, China"}]}],"member":"320","published-online":{"date-parts":[[2023,10,21]]},"reference":[{"key":"e_1_3_2_2_1_1","first-page":"1","article-title":"GluonTS: Probabilistic and Neural Time Series Modeling in Python","volume":"21","author":"Alexandrov Alexander","year":"2020","unstructured":"Alexander Alexandrov , Konstantinos Benidis , Michael Bohlke-Schneider , Valentin Flunkert , Jan Gasthaus , Tim Januschowski , Danielle C. Maddix , Syama Rangapuram , David Salinas , Jasper Schulz , Lorenzo Stella , Ali Caner T\u00fcrkmen , and Yuyang Wang . 2020 . GluonTS: Probabilistic and Neural Time Series Modeling in Python . Journal of Machine Learning Research , Vol. 21 , 116 (2020), 1 -- 6 . http:\/\/jmlr.org\/papers\/v21\/19--820.html Alexander Alexandrov, Konstantinos Benidis, Michael Bohlke-Schneider, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Danielle C. Maddix, Syama Rangapuram, David Salinas, Jasper Schulz, Lorenzo Stella, Ali Caner T\u00fcrkmen, and Yuyang Wang. 2020. GluonTS: Probabilistic and Neural Time Series Modeling in Python. Journal of Machine Learning Research, Vol. 21, 116 (2020), 1--6. http:\/\/jmlr.org\/papers\/v21\/19--820.html","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_2_2_1","volume-title":"An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271","author":"Bai Shaojie","year":"2018","unstructured":"Shaojie Bai , J Zico Kolter , and Vladlen Koltun . 2018. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271 ( 2018 ). Shaojie Bai, J Zico Kolter, and Vladlen Koltun. 2018. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271 (2018)."},{"key":"e_1_3_2_2_3_1","volume-title":"Numerical methods for ordinary differential equations","author":"Butcher John Charles","unstructured":"John Charles Butcher . 2016. Numerical methods for ordinary differential equations . John Wiley & Sons . John Charles Butcher. 2016. Numerical methods for ordinary differential equations. John Wiley & Sons."},{"key":"e_1_3_2_2_4_1","volume-title":"Recurrent neural networks for multivariate time series with missing values. Scientific reports","author":"Che Zhengping","year":"2018","unstructured":"Zhengping Che , Sanjay Purushotham , Kyunghyun Cho , David Sontag , and Yan Liu . 2018. Recurrent neural networks for multivariate time series with missing values. Scientific reports , Vol. 8 , 1 ( 2018 ), 1--12. Zhengping Che, Sanjay Purushotham, Kyunghyun Cho, David Sontag, and Yan Liu. 2018. Recurrent neural networks for multivariate time series with missing values. Scientific reports, Vol. 8, 1 (2018), 1--12."},{"key":"e_1_3_2_2_5_1","volume-title":"Garnett (Eds.)","volume":"31","author":"Chen Ricky T. Q.","year":"2018","unstructured":"Ricky T. Q. Chen , Yulia Rubanova , Jesse Bettencourt , and David K Duvenaud . 2018 . Neural Ordinary Differential Equations. In Advances in Neural Information Processing Systems, S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R . Garnett (Eds.) , Vol. 31 . Curran Associates, Inc., 1--13. https:\/\/proceedings.neurips.cc\/paper\/ 2018\/file\/69386f6bb1dfed68692a24c8686939b9-Paper.pdf Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud. 2018. Neural Ordinary Differential Equations. In Advances in Neural Information Processing Systems, S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett (Eds.), Vol. 31. Curran Associates, Inc., 1--13. https:\/\/proceedings.neurips.cc\/paper\/2018\/file\/69386f6bb1dfed68692a24c8686939b9-Paper.pdf"},{"key":"e_1_3_2_2_6_1","volume-title":"Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555","author":"Chung Junyoung","year":"2014","unstructured":"Junyoung Chung , Caglar Gulcehre , KyungHyun Cho , and Yoshua Bengio . 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 ( 2014 ). Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)."},{"key":"e_1_3_2_2_7_1","volume-title":"Stochastic Processes. In Proceedings of the Fourth Berkeley symposium on mathematical statistics and probability","volume":"4","author":"Cramer Harald","year":"1961","unstructured":"Harald Cramer . 1961 . Stochastic Processes. In Proceedings of the Fourth Berkeley symposium on mathematical statistics and probability , Vol. 4 . Univ of California Press, 57. Harald Cramer. 1961. Stochastic Processes. In Proceedings of the Fourth Berkeley symposium on mathematical statistics and probability, Vol. 4. Univ of California Press, 57."},{"key":"e_1_3_2_2_8_1","first-page":"6696","article-title":"Neural controlled differential equations for irregular time series","volume":"33","author":"Kidger Patrick","year":"2020","unstructured":"Patrick Kidger , James Morrill , James Foster , and Terry Lyons . 2020 . Neural controlled differential equations for irregular time series . Advances in Neural Information Processing Systems , Vol. 33 (2020), 6696 -- 6707 . Patrick Kidger, James Morrill, James Foster, and Terry Lyons. 2020. Neural controlled differential equations for irregular time series. Advances in Neural Information Processing Systems, Vol. 33 (2020), 6696--6707.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_9_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma and Jimmy Ba . 2014 . Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014). Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"e_1_3_2_2_10_1","volume-title":"Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting. Advances in neural information processing systems","author":"Li Shiyang","year":"2019","unstructured":"Shiyang Li , Xiaoyong Jin , Yao Xuan , Xiyou Zhou , Wenhu Chen , Yu-Xiang Wang , and Xifeng Yan . 2019. Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting. Advances in neural information processing systems , Vol. 32 ( 2019 ). Shiyang Li, Xiaoyong Jin, Yao Xuan, Xiyou Zhou, Wenhu Chen, Yu-Xiang Wang, and Xifeng Yan. 2019. Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting. Advances in neural information processing systems, Vol. 32 (2019)."},{"key":"e_1_3_2_2_11_1","unstructured":"Lev Semenovich Pontryagin EF Mishchenko VG Boltyanskii and RV Gamkrelidze. 1962. The mathematical theory of optimal processes.  Lev Semenovich Pontryagin EF Mishchenko VG Boltyanskii and RV Gamkrelidze. 1962. The mathematical theory of optimal processes."},{"key":"e_1_3_2_2_12_1","volume-title":"Advances in Neural Information Processing Systems","volume":"32","author":"Rubanova Yulia","year":"2019","unstructured":"Yulia Rubanova , Ricky T. Q. Chen , and David K Duvenaud . 2019 . Latent Ordinary Differential Equations for Irregularly-Sampled Time Series . In Advances in Neural Information Processing Systems , Vol. 32 . Curran Associates, Inc. https:\/\/proceedings.neurips.cc\/paper\/ 2019\/file\/42a6845a557bef704ad8ac9cb4461d43-Paper.pdf Yulia Rubanova, Ricky T. Q. Chen, and David K Duvenaud. 2019. Latent Ordinary Differential Equations for Irregularly-Sampled Time Series. In Advances in Neural Information Processing Systems, Vol. 32. Curran Associates, Inc. https:\/\/proceedings.neurips.cc\/paper\/2019\/file\/42a6845a557bef704ad8ac9cb4461d43-Paper.pdf"},{"key":"e_1_3_2_2_13_1","volume-title":"Sequence to sequence learning with neural networks. Advances in neural information processing systems","author":"Sutskever Ilya","year":"2014","unstructured":"Ilya Sutskever , Oriol Vinyals , and Quoc V Le. 2014. Sequence to sequence learning with neural networks. Advances in neural information processing systems , Vol. 27 ( 2014 ). Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014. Sequence to sequence learning with neural networks. Advances in neural information processing systems, Vol. 27 (2014)."},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1080\/00031305.2017.1380080"},{"key":"e_1_3_2_2_15_1","volume-title":"Attention is all you need. Advances in neural information processing systems","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani , Noam Shazeer , Niki Parmar , Jakob Uszkoreit , Llion Jones , Aidan N Gomez , \u0141ukasz Kaiser , and Illia Polosukhin . 2017. Attention is all you need. Advances in neural information processing systems , Vol. 30 ( 2017 ). Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_2_16_1","volume-title":"Transformers in time series: A survey. arXiv preprint arXiv:2202.07125","author":"Wen Qingsong","year":"2022","unstructured":"Qingsong Wen , Tian Zhou , Chaoli Zhang , Weiqi Chen , Ziqing Ma , Junchi Yan , and Liang Sun . 2022. Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 ( 2022 ). Qingsong Wen, Tian Zhou, Chaoli Zhang, Weiqi Chen, Ziqing Ma, Junchi Yan, and Liang Sun. 2022. Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022)."},{"key":"e_1_3_2_2_17_1","first-page":"22419","article-title":"Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting","volume":"34","author":"Wu Haixu","year":"2021","unstructured":"Haixu Wu , Jiehui Xu , Jianmin Wang , and Mingsheng Long . 2021 . Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting . Advances in Neural Information Processing Systems , Vol. 34 (2021), 22419 -- 22430 . Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long. 2021. Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. Advances in Neural Information Processing Systems, Vol. 34 (2021), 22419--22430.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2022.3230980"},{"key":"e_1_3_2_2_19_1","volume-title":"Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting. In The Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Virtual Conference","volume":"35","author":"Zhou Haoyi","year":"2021","unstructured":"Haoyi Zhou , Shanghang Zhang , Jieqi Peng , Shuai Zhang , Jianxin Li , Hui Xiong , and Wancai Zhang . 2021 . Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting. In The Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Virtual Conference , Vol. 35 . 11106--11115. Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting. In The Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Virtual Conference, Vol. 35. 11106--11115."},{"key":"e_1_3_2_2_20_1","volume-title":"Proceedings of the 39th International Conference on Machine Learning (Proceedings of Machine Learning Research","volume":"27286","author":"Zhou Tian","year":"2022","unstructured":"Tian Zhou , Ziqing Ma , Qingsong Wen , Xue Wang , Liang Sun , and Rong Jin . 2022 . FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting . In Proceedings of the 39th International Conference on Machine Learning (Proceedings of Machine Learning Research , Vol. 162). PMLR, 27268-- 27286 . https:\/\/proceedings.mlr.press\/v162\/zhou22g.html Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting. In Proceedings of the 39th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 162). PMLR, 27268--27286. https:\/\/proceedings.mlr.press\/v162\/zhou22g.html"},{"key":"e_1_3_2_2_21_1","volume-title":"pTSE: A Multi-model Ensemble Method for Probabilistic Time Series Forecasting. arXiv preprint arXiv:2305.11304","author":"Zhou Yunyi","year":"2023","unstructured":"Yunyi Zhou , Zhixuan Chu , Yijia Ruan , Ge Jin , Yuchen Huang , and Sheng Li. 2023. pTSE: A Multi-model Ensemble Method for Probabilistic Time Series Forecasting. arXiv preprint arXiv:2305.11304 ( 2023 ). Yunyi Zhou, Zhixuan Chu, Yijia Ruan, Ge Jin, Yuchen Huang, and Sheng Li. 2023. pTSE: A Multi-model Ensemble Method for Probabilistic Time Series Forecasting. arXiv preprint arXiv:2305.11304 (2023)."}],"event":{"name":"CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management","location":"Birmingham United Kingdom","acronym":"CIKM '23","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 32nd ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3583780.3615487","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3583780.3615487","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:36:54Z","timestamp":1750178214000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3583780.3615487"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,21]]},"references-count":21,"alternative-id":["10.1145\/3583780.3615487","10.1145\/3583780"],"URL":"https:\/\/doi.org\/10.1145\/3583780.3615487","relation":{},"subject":[],"published":{"date-parts":[[2023,10,21]]},"assertion":[{"value":"2023-10-21","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}