{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T20:59:20Z","timestamp":1765486760385,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":26,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,11,3]],"date-time":"2021-11-03T00:00:00Z","timestamp":1635897600000},"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":[[2021,11,3]]},"DOI":"10.1145\/3490354.3494404","type":"proceedings-article","created":{"date-parts":[[2022,5,5]],"date-time":"2022-05-05T03:50:06Z","timestamp":1651722606000},"page":"1-7","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Deep video prediction for time series forecasting"],"prefix":"10.1145","author":[{"given":"Zhen","family":"Zeng","sequence":"first","affiliation":[{"name":"J. P. Morgan AI Research"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tucker","family":"Balch","sequence":"additional","affiliation":[{"name":"J. P. Morgan AI Research"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Manuela","family":"Veloso","sequence":"additional","affiliation":[{"name":"J. P. Morgan AI Research"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,5,4]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Stochastic variational video prediction. arXiv preprint arXiv:1710.11252","author":"Babaeizadeh Mohammad","year":"2017","unstructured":"Mohammad Babaeizadeh , Chelsea Finn , Dumitru Erhan , Roy H Campbell , and Sergey Levine . 2017. Stochastic variational video prediction. arXiv preprint arXiv:1710.11252 ( 2017 ). Mohammad Babaeizadeh, Chelsea Finn, Dumitru Erhan, Roy H Campbell, and Sergey Levine. 2017. Stochastic variational video prediction. arXiv preprint arXiv:1710.11252 (2017)."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3383455.3422544"},{"key":"e_1_3_2_1_3_1","volume-title":"Stochastic video generation with a learned prior. arXiv preprint arXiv:1802.07687","author":"Denton Emily","year":"2018","unstructured":"Emily Denton and Rob Fergus . 2018. Stochastic video generation with a learned prior. arXiv preprint arXiv:1802.07687 ( 2018 ). Emily Denton and Rob Fergus. 2018. Stochastic video generation with a learned prior. arXiv preprint arXiv:1802.07687 (2018)."},{"key":"e_1_3_2_1_4_1","volume-title":"Image Processing Tools for Financial Time Series Classification. arXiv preprint arXiv:2008.06042","author":"Du Bairui","year":"2020","unstructured":"Bairui Du and Paolo Barucca . 2020. Image Processing Tools for Financial Time Series Classification. arXiv preprint arXiv:2008.06042 ( 2020 ). Bairui Du and Paolo Barucca. 2020. Image Processing Tools for Financial Time Series Classification. arXiv preprint arXiv:2008.06042 (2020)."},{"key":"e_1_3_2_1_5_1","volume-title":"Self-supervised visual planning with temporal skip connections. arXiv preprint arXiv:1710.05268","author":"Ebert Frederik","year":"2017","unstructured":"Frederik Ebert , Chelsea Finn , Alex X Lee , and Sergey Levine . 2017. Self-supervised visual planning with temporal skip connections. arXiv preprint arXiv:1710.05268 ( 2017 ). Frederik Ebert, Chelsea Finn, Alex X Lee, and Sergey Levine. 2017. Self-supervised visual planning with temporal skip connections. arXiv preprint arXiv:1710.05268 (2017)."},{"key":"e_1_3_2_1_6_1","unstructured":"Chelsea Finn Ian Goodfellow and Sergey Levine. 2016. Unsupervised learning for physical interaction through video prediction. In Advances in neural information processing systems. 64--72.  Chelsea Finn Ian Goodfellow and Sergey Levine. 2016. Unsupervised learning for physical interaction through video prediction. In Advances in neural information processing systems. 64--72."},{"key":"e_1_3_2_1_7_1","volume-title":"Stochastic Latent Residual Video Prediction. arXiv preprint arXiv:2002.09219","author":"Franceschi Jean-Yves","year":"2020","unstructured":"Jean-Yves Franceschi , Edouard Delasalles , Micka\u00ebl Chen , Sylvain Lamprier , and Patrick Gallinari . 2020. Stochastic Latent Residual Video Prediction. arXiv preprint arXiv:2002.09219 ( 2020 ). Jean-Yves Franceschi, Edouard Delasalles, Micka\u00ebl Chen, Sylvain Lamprier, and Patrick Gallinari. 2020. Stochastic Latent Residual Video Prediction. arXiv preprint arXiv:2002.09219 (2020)."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.31.10.1237"},{"key":"e_1_3_2_1_9_1","volume-title":"International journal of forecasting 20, 1","author":"Holt Charles C","year":"2004","unstructured":"Charles C Holt . 2004. Forecasting seasonals and trends by exponentially weighted moving averages . International journal of forecasting 20, 1 ( 2004 ), 5--10. Charles C Holt. 2004. Forecasting seasonals and trends by exponentially weighted moving averages. International journal of forecasting 20, 1 (2004), 5--10."},{"key":"e_1_3_2_1_10_1","unstructured":"Jun-Ting Hsieh Bingbin Liu De-An Huang Li F Fei-Fei and Juan Carlos Niebles. 2018. Learning to decompose and disentangle representations for video prediction. In Advances in Neural Information Processing Systems. 517--526.  Jun-Ting Hsieh Bingbin Liu De-An Huang Li F Fei-Fei and Juan Carlos Niebles. 2018. Learning to decompose and disentangle representations for video prediction. In Advances in Neural Information Processing Systems. 517--526."},{"key":"e_1_3_2_1_11_1","unstructured":"Rob J Hyndman and George Athanasopoulos. 2018. Forecasting: principles and practice. OTexts.  Rob J Hyndman and George Athanasopoulos. 2018. Forecasting: principles and practice. OTexts."},{"key":"e_1_3_2_1_12_1","volume-title":"6m: Large scale datasets and predictive methods for 3d human sensing in natural environments","author":"Ionescu Catalin","year":"2013","unstructured":"Catalin Ionescu , Dragos Papava , Vlad Olaru , and Cristian Sminchisescu . 2013. Human3. 6m: Large scale datasets and predictive methods for 3d human sensing in natural environments . IEEE transactions on pattern analysis and machine intelligence 36, 7 ( 2013 ), 1325--1339. Catalin Ionescu, Dragos Papava, Vlad Olaru, and Cristian Sminchisescu. 2013. Human3. 6m: Large scale datasets and predictive methods for 3d human sensing in natural environments. IEEE transactions on pattern analysis and machine intelligence 36, 7 (2013), 1325--1339."},{"volume-title":"Advances in Neural Information Processing Systems 25","author":"Krizhevsky Alex","key":"e_1_3_2_1_13_1","unstructured":"Alex Krizhevsky , Ilya Sutskever , and Geoffrey E Hinton . 2012. ImageNet Classification with Deep Convolutional Neural Networks . In Advances in Neural Information Processing Systems 25 , F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger (Eds.). Curran Associates, Inc. , 1097--1105. http:\/\/papers.nips.cc\/paper\/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012. ImageNet Classification with Deep Convolutional Neural Networks. In Advances in Neural Information Processing Systems 25, F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger (Eds.). Curran Associates, Inc., 1097--1105. http:\/\/papers.nips.cc\/paper\/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113680"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2018.06.001"},{"key":"e_1_3_2_1_16_1","volume-title":"Sergio Orts-Escolano, Jose Garcia-Rodriguez, and Antonis Argyros.","author":"Oprea Sergiu","year":"2020","unstructured":"Sergiu Oprea , Pablo Martinez-Gonzalez , Alberto Garcia-Garcia , John Alejandro Castro-Vargas , Sergio Orts-Escolano, Jose Garcia-Rodriguez, and Antonis Argyros. 2020 . A Review on Deep Learning Techniques for Video Prediction . arXiv preprint arXiv:2004.05214 (2020). Sergiu Oprea, Pablo Martinez-Gonzalez, Alberto Garcia-Garcia, John Alejandro Castro-Vargas, Sergio Orts-Escolano, Jose Garcia-Rodriguez, and Antonis Argyros. 2020. A Review on Deep Learning Techniques for Video Prediction. arXiv preprint arXiv:2004.05214 (2020)."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.omega.2004.07.024"},{"key":"e_1_3_2_1_18_1","volume-title":"a baseline for generative models of natural videos. arXiv preprint arXiv:1412.6604","author":"Ranzato MarcAurelio","year":"2014","unstructured":"MarcAurelio Ranzato , Arthur Szlam , Joan Bruna , Michael Mathieu , Ronan Collobert , and Sumit Chopra . 2014. Video (language) modeling : a baseline for generative models of natural videos. arXiv preprint arXiv:1412.6604 ( 2014 ). MarcAurelio Ranzato, Arthur Szlam, Joan Bruna, Michael Mathieu, Ronan Collobert, and Sumit Chopra. 2014. Video (language) modeling: a baseline for generative models of natural videos. arXiv preprint arXiv:1412.6604 (2014)."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_44"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2018.01.007"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2004.1334462"},{"key":"e_1_3_2_1_22_1","volume-title":"DeepInsight: A methodology to transform a non-image data to an image for convolution neural network architecture. Scientific reports 9, 1","author":"Sharma Alok","year":"2019","unstructured":"Alok Sharma , Edwin Vans , Daichi Shigemizu , Keith A Boroevich , and Tatsuhiko Tsunoda . 2019. DeepInsight: A methodology to transform a non-image data to an image for convolution neural network architecture. Scientific reports 9, 1 ( 2019 ), 1--7. Alok Sharma, Edwin Vans, Daichi Shigemizu, Keith A Boroevich, and Tatsuhiko Tsunoda. 2019. DeepInsight: A methodology to transform a non-image data to an image for convolution neural network architecture. Scientific reports 9, 1 (2019), 1--7."},{"key":"e_1_3_2_1_23_1","volume-title":"Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556","author":"Simonyan Karen","year":"2014","unstructured":"Karen Simonyan and Andrew Zisserman . 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 ( 2014 ). Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)."},{"key":"e_1_3_2_1_24_1","volume-title":"Forecasting sales by exponentially weighted moving averages. Management science 6, 3","author":"Winters Peter R","year":"1960","unstructured":"Peter R Winters . 1960. Forecasting sales by exponentially weighted moving averages. Management science 6, 3 ( 1960 ), 324--342. Peter R Winters. 1960. Forecasting sales by exponentially weighted moving averages. Management science 6, 3 (1960), 324--342."},{"key":"e_1_3_2_1_25_1","unstructured":"Manzil Zaheer Satwik Kottur Siamak Ravanbakhsh Barnabas Poczos Russ R Salakhutdinov and Alexander J Smola. 2017. Deep sets. In Advances in neural information processing systems. 3391--3401.  Manzil Zaheer Satwik Kottur Siamak Ravanbakhsh Barnabas Poczos Russ R Salakhutdinov and Alexander J Smola. 2017. Deep sets. In Advances in neural information processing systems. 3391--3401."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0925-2312(01)00702-0"}],"event":{"name":"ICAIF'21: 2nd ACM International Conference on AI in Finance","sponsor":["ACM Association for Computing Machinery"],"location":"Virtual Event","acronym":"ICAIF'21"},"container-title":["Proceedings of the Second ACM International Conference on AI in Finance"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3490354.3494404","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3490354.3494404","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:30:42Z","timestamp":1750188642000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3490354.3494404"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,3]]},"references-count":26,"alternative-id":["10.1145\/3490354.3494404","10.1145\/3490354"],"URL":"https:\/\/doi.org\/10.1145\/3490354.3494404","relation":{},"subject":[],"published":{"date-parts":[[2021,11,3]]},"assertion":[{"value":"2022-05-04","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}