{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T12:51:03Z","timestamp":1774702263771,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":46,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T00:00:00Z","timestamp":1665964800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001843","name":"Science and Engineering Research Board","doi-asserted-by":"publisher","award":["IITPKD\/2021\/013\/CSE\/SAB"],"award-info":[{"award-number":["IITPKD\/2021\/013\/CSE\/SAB"]}],"id":[{"id":"10.13039\/501100001843","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,10,17]]},"DOI":"10.1145\/3511808.3557282","type":"proceedings-article","created":{"date-parts":[[2022,10,16]],"date-time":"2022-10-16T01:29:57Z","timestamp":1665883797000},"page":"1726-1735","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Deep Extreme Mixture Model for Time Series Forecasting"],"prefix":"10.1145","author":[{"given":"Abilasha","family":"S","sequence":"first","affiliation":[{"name":"Indian Institute of Technology, Palakkad, Palakkad, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sahely","family":"Bhadra","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology, Palakkad, Palakkad, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmed Zaheer","family":"Dadarkar","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology, Palakkad, Palakkad, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Deepak","family":"P","sequence":"additional","affiliation":[{"name":"Queen's University Belfast, Belfast, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,10,17]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Forecasting Drought Using Multilayer Perceptron Artificial Neural Network Model. Advances in Meteorology","volume":"2017","author":"Zulifqar","year":"2017","unstructured":"Zulifqar Ali et al. 2017 . Forecasting Drought Using Multilayer Perceptron Artificial Neural Network Model. Advances in Meteorology , Vol. 2017 (02 May 2017 ), 5681308. https:\/\/doi.org\/10.1155\/2017\/5681308 10.1155\/2017 Zulifqar Ali et al. 2017. Forecasting Drought Using Multilayer Perceptron Artificial Neural Network Model. Advances in Meteorology, Vol. 2017 (02 May 2017), 5681308. https:\/\/doi.org\/10.1155\/2017\/5681308"},{"key":"e_1_3_2_2_2_1","volume-title":"Stock Price Prediction Using the ARIMA Model. In 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation. 106--112","author":"Ariyo Adebiyi A.","year":"2014","unstructured":"Adebiyi A. Ariyo , Adewumi O. Adewumi , and Charles K. Ayo . 2014 . Stock Price Prediction Using the ARIMA Model. In 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation. 106--112 . https:\/\/doi.org\/10.1109\/UKSim. 2014 .67 10.1109\/UKSim.2014.67 Adebiyi A. Ariyo, Adewumi O. Adewumi, and Charles K. Ayo. 2014. Stock Price Prediction Using the ARIMA Model. In 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation. 106--112. https:\/\/doi.org\/10.1109\/UKSim.2014.67"},{"key":"e_1_3_2_2_3_1","unstructured":"Percy artist Moran and Peter Whittle. 1951. Hypothesis Testing in Time Series Analysis.  Percy artist Moran and Peter Whittle. 1951. Hypothesis Testing in Time Series Analysis."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1093\/jjfinec\/nbz003"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.2307\/2985674"},{"key":"e_1_3_2_2_6_1","unstructured":"R.G. Brown. 1956. Exponential Smoothing for Predicting Demand. Little. https:\/\/books.google.co.in\/books?id=Eo_rMgEACAAJ  R.G. Brown. 1956. Exponential Smoothing for Predicting Demand. Little. https:\/\/books.google.co.in\/books?id=Eo_rMgEACAAJ"},{"key":"e_1_3_2_2_7_1","volume-title":"Atmospheric CO2 concentrations derived from flask air samples at sites in the SIO network. Trends: a compendium of data on Global Change","author":"Charles David Keeling","year":"2004","unstructured":"David Keeling Charles and Timothy P Whorf . 2004. Atmospheric CO2 concentrations derived from flask air samples at sites in the SIO network. Trends: a compendium of data on Global Change ( 2004 ). David Keeling Charles and Timothy P Whorf. 2004. Atmospheric CO2 concentrations derived from flask air samples at sites in the SIO network. Trends: a compendium of data on Global Change (2004)."},{"key":"e_1_3_2_2_8_1","volume-title":"International conference on machine learning. PMLR, 893--902","author":"Chen Liqun","year":"2018","unstructured":"Liqun Chen , Chenyang Tao , Ruiyi Zhang , Ricardo Henao , and Lawrence Carin Duke . 2018 . Variational inference and model selection with generalized evidence bounds . In International conference on machine learning. PMLR, 893--902 . Liqun Chen, Chenyang Tao, Ruiyi Zhang, Ricardo Henao, and Lawrence Carin Duke. 2018. Variational inference and model selection with generalized evidence bounds. In International conference on machine learning. PMLR, 893--902."},{"key":"e_1_3_2_2_9_1","volume-title":"EvaNet: An Extreme Value Attention Network for Long-Term Air Quality Prediction. In 2020 IEEE International Conference on Big Data (Big Data). 4545--4552","author":"Chen Z.","year":"2020","unstructured":"Z. Chen , H. Yu , Y. a. Geng , Q. Li , and Y. Zhang . 2020 . EvaNet: An Extreme Value Attention Network for Long-Term Air Quality Prediction. In 2020 IEEE International Conference on Big Data (Big Data). 4545--4552 . https:\/\/doi.org\/10.1109\/BigData50022. 2020 .9378094 10.1109\/BigData50022.2020.9378094 Z. Chen, H. Yu, Y. a. Geng, Q. Li, and Y. Zhang. 2020. EvaNet: An Extreme Value Attention Network for Long-Term Air Quality Prediction. In 2020 IEEE International Conference on Big Data (Big Data). 4545--4552. https:\/\/doi.org\/10.1109\/BigData50022.2020.9378094"},{"key":"e_1_3_2_2_10_1","volume-title":"An introduction to statistical modeling of extreme values","author":"Coles Stuart","unstructured":"Stuart Coles . 2001. An introduction to statistical modeling of extreme values . Springer-Verlag , London . Stuart Coles. 2001. An introduction to statistical modeling of extreme values. Springer-Verlag, London."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF00894450"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12040-020-01408-x"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330896"},{"key":"e_1_3_2_2_14_1","volume-title":"Probabilistic time series forecasting with structured shape and temporal diversity. arXiv preprint arXiv:2010.07349","author":"Guen Vincent Le","year":"2020","unstructured":"Vincent Le Guen and Nicolas Thome . 2020. Probabilistic time series forecasting with structured shape and temporal diversity. arXiv preprint arXiv:2010.07349 ( 2020 ). Vincent Le Guen and Nicolas Thome. 2020. Probabilistic time series forecasting with structured shape and temporal diversity. arXiv preprint arXiv:2010.07349 (2020)."},{"key":"e_1_3_2_2_15_1","volume-title":"Robust Online Time Series Prediction with Recurrent Neural Networks. In 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA). 816--825","author":"Guo Tian","year":"2016","unstructured":"Tian Guo , Zhao Xu , Xin Yao , Haifeng Chen , Karl Aberer , and Koichi Funaya . 2016 . Robust Online Time Series Prediction with Recurrent Neural Networks. In 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA). 816--825 . https:\/\/doi.org\/10.1109\/DSAA.2016.92 10.1109\/DSAA.2016.92 Tian Guo, Zhao Xu, Xin Yao, Haifeng Chen, Karl Aberer, and Koichi Funaya. 2016. Robust Online Time Series Prediction with Recurrent Neural Networks. In 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA). 816--825. https:\/\/doi.org\/10.1109\/DSAA.2016.92"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/SCES.2012.6199099"},{"key":"e_1_3_2_2_17_1","volume-title":"Proceedings. 5th International Conference on Intelligent Systems Design and Applications. IEEE Computer Society","author":"Hassan M.","year":"2005","unstructured":"M. Hassan and B. Nath . 2005. StockMarket Forecasting Using Hidden Markov Model: A New Approach . In Proceedings. 5th International Conference on Intelligent Systems Design and Applications. IEEE Computer Society , Los Alamitos, CA, USA, 192--196. https:\/\/doi.org\/10.1109\/ISDA. 2005 .85 10.1109\/ISDA.2005.85 M. Hassan and B. Nath. 2005. StockMarket Forecasting Using Hidden Markov Model: A New Approach. In Proceedings. 5th International Conference on Intelligent Systems Design and Applications. IEEE Computer Society, Los Alamitos, CA, USA, 192--196. https:\/\/doi.org\/10.1109\/ISDA.2005.85"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2957806"},{"key":"e_1_3_2_2_19_1","article-title":"Automatic Time Series Forecasting: The forecast Package for R","volume":"26","author":"Hyndman Rob","year":"2008","unstructured":"Rob Hyndman and Yeasmin Khandakar . 2008 . Automatic Time Series Forecasting: The forecast Package for R . Journal of Statistical Software , Vol. 26 (07 2008). https:\/\/doi.org\/10.18637\/jss.v027.i03 10.18637\/jss.v027.i03 Rob Hyndman and Yeasmin Khandakar. 2008. Automatic Time Series Forecasting: The forecast Package for R. Journal of Statistical Software, Vol. 26 (07 2008). https:\/\/doi.org\/10.18637\/jss.v027.i03","journal-title":"Journal of Statistical Software"},{"key":"e_1_3_2_2_20_1","volume-title":"Forecasting with exponential smoothing: the state space approach","author":"Hyndman Rob","unstructured":"Rob Hyndman , Anne B Koehler , J Keith Ord , and Ralph D Snyder . 2008. Forecasting with exponential smoothing: the state space approach . Springer Science & Business Media . Rob Hyndman, Anne B Koehler, J Keith Ord, and Ralph D Snyder. 2008. Forecasting with exponential smoothing: the state space approach. Springer Science & Business Media."},{"key":"e_1_3_2_2_21_1","volume-title":"Proceedings of ICNN'95 - International Conference on Neural Networks","volume":"4","author":"Kennedy J.","year":"1942","unstructured":"J. Kennedy and R. Eberhart . 1995. Particle swarm optimization . In Proceedings of ICNN'95 - International Conference on Neural Networks , Vol. 4 . 1942 --1948 vol.4. https:\/\/doi.org\/10.1109\/ICNN.1995.488968 10.1109\/ICNN.1995.488968 J. Kennedy and R. Eberhart. 1995. Particle swarm optimization. In Proceedings of ICNN'95 - International Conference on Neural Networks, Vol. 4. 1942--1948 vol.4. https:\/\/doi.org\/10.1109\/ICNN.1995.488968"},{"key":"e_1_3_2_2_22_1","unstructured":"Diederik Kingma and Max Welling. 2014. Auto-Encoding Variational Bayes.  Diederik Kingma and Max Welling. 2014. Auto-Encoding Variational Bayes."},{"key":"e_1_3_2_2_23_1","volume-title":"Improved variational inference with inverse autoregressive flow. Advances in neural information processing systems","author":"Kingma Durk P","year":"2016","unstructured":"Durk P Kingma , Tim Salimans , Rafal Jozefowicz , Xi Chen , Ilya Sutskever , and Max Welling . 2016. Improved variational inference with inverse autoregressive flow. Advances in neural information processing systems , Vol. 29 ( 2016 ). Durk P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling. 2016. Improved variational inference with inverse autoregressive flow. Advances in neural information processing systems, Vol. 29 (2016)."},{"key":"e_1_3_2_2_24_1","volume-title":"Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114","author":"Kingma Diederik P","year":"2013","unstructured":"Diederik P Kingma and Max Welling . 2013. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 ( 2013 ). Diederik P Kingma and Max Welling. 2013. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)."},{"key":"e_1_3_2_2_25_1","volume-title":"Deepak Kumar Gupta, and Samayveer Singh","author":"Kumar Manish","year":"2021","unstructured":"Manish Kumar , Deepak Kumar Gupta, and Samayveer Singh . 2021 . Extreme Event Forecasting Using Machine Learning Models. In Advances in Communication and Computational Technology, Gurdeep Singh Hura, Ashutosh Kumar Singh, and Lau Siong Hoe (Eds.). Springer Singapore , Singapore, 1503--1514. Manish Kumar, Deepak Kumar Gupta, and Samayveer Singh. 2021. Extreme Event Forecasting Using Machine Learning Models. In Advances in Communication and Computational Technology, Gurdeep Singh Hura, Ashutosh Kumar Singh, and Lau Siong Hoe (Eds.). Springer Singapore, Singapore, 1503--1514."},{"key":"e_1_3_2_2_26_1","volume-title":"International conference on machine learning","volume":"34","author":"Laptev Nikolay","year":"2017","unstructured":"Nikolay Laptev , Jason Yosinski , Li Erran Li , and Slawek Smyl . 2017 . Time-series extreme event forecasting with neural networks at uber . In International conference on machine learning , Vol. 34 . sn, 1--5. Nikolay Laptev, Jason Yosinski, Li Erran Li, and Slawek Smyl. 2017. Time-series extreme event forecasting with neural networks at uber. In International conference on machine learning, Vol. 34. sn, 1--5."},{"key":"e_1_3_2_2_27_1","volume-title":"Shape and time distortion loss for training deep time series forecasting models. Advances in neural information processing systems","author":"Guen Vincent Le","year":"2019","unstructured":"Vincent Le Guen and Nicolas Thome . 2019. Shape and time distortion loss for training deep time series forecasting models. Advances in neural information processing systems , Vol. 32 ( 2019 ). Vincent Le Guen and Nicolas Thome. 2019. Shape and time distortion loss for training deep time series forecasting models. Advances in neural information processing systems, Vol. 32 (2019)."},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.3389\/fmars.2021.798408"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/72.548162"},{"key":"e_1_3_2_2_30_1","volume-title":"Garnett (Eds.)","volume":"28","author":"Liu Yu-Ying","year":"2015","unstructured":"Yu-Ying Liu , Shuang Li , Fuxin Li , Le Song , and James M Rehg . 2015 . Efficient Learning of Continuous-Time Hidden Markov Models for Disease Progression. In Advances in Neural Information Processing Systems,, C. Cortes, N. Lawrence, D. Lee, M. Sugiyama, and R . Garnett (Eds.) , Vol. 28 . Curran Associates, Inc. https:\/\/proceedings.neurips.cc\/paper\/ 2015\/file\/a591024321c5e2bdbd23ed35f0574dde-Paper.pdf Yu-Ying Liu, Shuang Li, Fuxin Li, Le Song, and James M Rehg. 2015. Efficient Learning of Continuous-Time Hidden Markov Models for Disease Progression. In Advances in Neural Information Processing Systems,, C. Cortes, N. Lawrence, D. Lee, M. Sugiyama, and R. Garnett (Eds.), Vol. 28. Curran Associates, Inc. https:\/\/proceedings.neurips.cc\/paper\/2015\/file\/a591024321c5e2bdbd23ed35f0574dde-Paper.pdf"},{"key":"e_1_3_2_2_31_1","volume-title":"International Conference on Machine Learning. PMLR, 2391--2400","author":"Mescheder Lars","year":"2017","unstructured":"Lars Mescheder , Sebastian Nowozin , and Andreas Geiger . 2017 . Adversarial variational bayes: Unifying variational autoencoders and generative adversarial networks . In International Conference on Machine Learning. PMLR, 2391--2400 . Lars Mescheder, Sebastian Nowozin, and Andreas Geiger. 2017. Adversarial variational bayes: Unifying variational autoencoders and generative adversarial networks. In International Conference on Machine Learning. PMLR, 2391--2400."},{"key":"e_1_3_2_2_32_1","volume-title":"N-BEATS: Neural basis expansion analysis for interpretable time series forecasting. arXiv preprint arXiv:1905.10437","author":"Oreshkin Boris N","year":"2019","unstructured":"Boris N Oreshkin , Dmitri Carpov , Nicolas Chapados , and Yoshua Bengio . 2019. N-BEATS: Neural basis expansion analysis for interpretable time series forecasting. arXiv preprint arXiv:1905.10437 ( 2019 ). Boris N Oreshkin, Dmitri Carpov, Nicolas Chapados, and Yoshua Bengio. 2019. N-BEATS: Neural basis expansion analysis for interpretable time series forecasting. arXiv preprint arXiv:1905.10437 (2019)."},{"key":"e_1_3_2_2_33_1","volume-title":"Statistical inference using extreme order statistics. the Annals of Statistics","author":"James Pickands III.","year":"1975","unstructured":"James Pickands III. 1975. Statistical inference using extreme order statistics. the Annals of Statistics ( 1975 ), 119--131. James Pickands III. 1975. Statistical inference using extreme order statistics. the Annals of Statistics (1975), 119--131."},{"key":"e_1_3_2_2_34_1","volume-title":"Garnett (Eds.)","volume":"31","author":"Rangapuram Syama Sundar","year":"2018","unstructured":"Syama Sundar Rangapuram , Matthias W Seeger , Jan Gasthaus , Lorenzo Stella , Yuyang Wang , and Tim Januschowski . 2018 . Deep State Space Models for Time Series Forecasting. 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. https:\/\/proceedings.neurips.cc\/paper\/ 2018\/file\/5cf68969fb67aa6082363a6d4e6468e2-Paper.pdf Syama Sundar Rangapuram, Matthias W Seeger, Jan Gasthaus, Lorenzo Stella, Yuyang Wang, and Tim Januschowski. 2018. Deep State Space Models for Time Series Forecasting. 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. https:\/\/proceedings.neurips.cc\/paper\/2018\/file\/5cf68969fb67aa6082363a6d4e6468e2-Paper.pdf"},{"key":"e_1_3_2_2_35_1","volume-title":"International conference on machine learning. PMLR, 1530--1538","author":"Rezende Danilo","year":"2015","unstructured":"Danilo Rezende and Shakir Mohamed . 2015 . Variational inference with normalizing flows . In International conference on machine learning. PMLR, 1530--1538 . Danilo Rezende and Shakir Mohamed. 2015. Variational inference with normalizing flows. In International conference on machine learning. PMLR, 1530--1538."},{"key":"e_1_3_2_2_36_1","volume-title":"Learning representations by back-propagating errors. nature","author":"Rumelhart David E","year":"1986","unstructured":"David E Rumelhart , Geoffrey E Hinton , and Ronald J Williams . 1986. Learning representations by back-propagating errors. nature , Vol. 323 , 6088 ( 1986 ), 533--536. David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams. 1986. Learning representations by back-propagating errors. nature, Vol. 323, 6088 (1986), 533--536."},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2019.07.001"},{"key":"e_1_3_2_2_38_1","volume-title":"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 . In Advances in Neural Information Processing Systems , I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.), Vol. 30 . Curran Associates, Inc. https:\/\/proceedings.neurips.cc\/paper\/ 2017 \/file\/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is All you Need. In Advances in Neural Information Processing Systems, I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.), Vol. 30. Curran Associates, Inc. https:\/\/proceedings.neurips.cc\/paper\/2017\/file\/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf"},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3402597.3402609"},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3402597.3402609"},{"key":"e_1_3_2_2_41_1","volume-title":"Unconstrained monotonic neural networks. Advances in neural information processing systems","author":"Wehenkel Antoine","year":"2019","unstructured":"Antoine Wehenkel and Gilles Louppe . 2019. Unconstrained monotonic neural networks. Advances in neural information processing systems , Vol. 32 ( 2019 ). Antoine Wehenkel and Gilles Louppe. 2019. Unconstrained monotonic neural networks. Advances in neural information processing systems, Vol. 32 (2019)."},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17253"},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2020.03.257"},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3108831"},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"e_1_3_2_2_46_1","volume-title":"2017 IEEE International Conference on Data Mining Workshops (ICDMW). 103--110","author":"Zhu L.","unstructured":"L. Zhu and N. Laptev . 2017. Deep and Confident Prediction for Time Series at Uber . In 2017 IEEE International Conference on Data Mining Workshops (ICDMW). 103--110 . L. Zhu and N. Laptev. 2017. Deep and Confident Prediction for Time Series at Uber. In 2017 IEEE International Conference on Data Mining Workshops (ICDMW). 103--110."}],"event":{"name":"CIKM '22: The 31st ACM International Conference on Information and Knowledge Management","location":"Atlanta GA USA","acronym":"CIKM '22","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 31st ACM International Conference on Information & Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3511808.3557282","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3511808.3557282","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:49:28Z","timestamp":1750182568000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3511808.3557282"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,17]]},"references-count":46,"alternative-id":["10.1145\/3511808.3557282","10.1145\/3511808"],"URL":"https:\/\/doi.org\/10.1145\/3511808.3557282","relation":{},"subject":[],"published":{"date-parts":[[2022,10,17]]},"assertion":[{"value":"2022-10-17","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}