{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T16:33:12Z","timestamp":1784392392312,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":33,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,7,25]],"date-time":"2019-07-25T00:00:00Z","timestamp":1564012800000},"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":[[2019,7,25]]},"DOI":"10.1145\/3292500.3330662","type":"proceedings-article","created":{"date-parts":[[2019,7,26]],"date-time":"2019-07-26T13:17:26Z","timestamp":1564147046000},"page":"2527-2535","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":179,"title":["Multi-Horizon Time Series Forecasting with Temporal Attention Learning"],"prefix":"10.1145","author":[{"given":"Chenyou","family":"Fan","sequence":"first","affiliation":[{"name":"JD.com, Mountain View, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuze","family":"Zhang","sequence":"additional","affiliation":[{"name":"JD.com, Mountain View, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Pan","sequence":"additional","affiliation":[{"name":"JD.com, Mountain View, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyue","family":"Li","sequence":"additional","affiliation":[{"name":"JD.com, Mountain View, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chi","family":"Zhang","sequence":"additional","affiliation":[{"name":"JD.com, Mountain View, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rong","family":"Yuan","sequence":"additional","affiliation":[{"name":"JD.com, Mountain View, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Di","family":"Wu","sequence":"additional","affiliation":[{"name":"JD.com, Mountain View, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wensheng","family":"Wang","sequence":"additional","affiliation":[{"name":"JD.com, Mountain View, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Pei","sequence":"additional","affiliation":[{"name":"JD.com & Simon Fraser University, Mountain View, CA, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heng","family":"Huang","sequence":"additional","affiliation":[{"name":"JD Finance America Corporation & University of Pittsburgh, Pittsburgh, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2019,7,25]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Mart'in Abadi Paul Barham Jianmin Chen Zhifeng Chen Andy Davis Jeffrey Dean Matthieu Devin Sanjay Ghemawat Geoffrey Irving Michael Isard et almbox. {n. d.}. Tensorflow: a system for large-scale machine learning.  Mart'in Abadi Paul Barham Jianmin Chen Zhifeng Chen Andy Davis Jeffrey Dean Matthieu Devin Sanjay Ghemawat Geoffrey Irving Michael Isard et almbox. {n. d.}. Tensorflow: a system for large-scale machine learning."},{"key":"e_1_3_2_1_2_1","volume-title":"Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473","author":"Bahdanau Dzmitry","year":"2014"},{"key":"e_1_3_2_1_3_1","volume-title":"International Conference on Machine Learning .","author":"Bahdanau Dzmitry","year":"2015"},{"key":"e_1_3_2_1_4_1","volume-title":"An overview and comparative analysis of recurrent neural networks for short term load forecasting. arXiv preprint arXiv:1705.04378","author":"Bianchi Filippo Maria","year":"2017"},{"key":"e_1_3_2_1_5_1","unstructured":"G. E. P. Box and G. M. Jenkins. 1968. Some Recent Advances in Forecasting and Control. Journal of the Royal Statistical Society (1968).  G. E. P. Box and G. M. Jenkins. 1968. Some Recent Advances in Forecasting and Control. Journal of the Royal Statistical Society (1968)."},{"key":"e_1_3_2_1_6_1","unstructured":"Yagmur Gizem Cinar Hamid Mirisaee Parantapa Goswami Eric Gaussier Ali A\"it-Bachir and Vadim Strijov. 2017. Position-based content attention for time series forecasting with sequence-to-sequence rnns. In Advances in Neural Information Processing Systems .  Yagmur Gizem Cinar Hamid Mirisaee Parantapa Goswami Eric Gaussier Ali A\"it-Bachir and Vadim Strijov. 2017. Position-based content attention for time series forecasting with sequence-to-sequence rnns. In Advances in Neural Information Processing Systems ."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298878"},{"key":"e_1_3_2_1_8_1","volume-title":"Finding structure in time. Cognitive science","author":"Elman Jeffrey L","year":"1990"},{"key":"e_1_3_2_1_9_1","unstructured":"Valentin Flunkert David Salinas and Jan Gasthaus. 2017. DeepAR: Probabilistic forecasting with autoregressive recurrent networks. arXiv preprint arXiv:1704.04110 .  Valentin Flunkert David Salinas and Jan Gasthaus. 2017. DeepAR: Probabilistic forecasting with autoregressive recurrent networks. arXiv preprint arXiv:1704.04110 ."},{"key":"e_1_3_2_1_10_1","volume-title":"Greedy function approximation: a gradient boosting machine. Annals of Statistics","author":"Friedman Jerome H","year":"2001"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.81"},{"key":"e_1_3_2_1_12_1","volume-title":"Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850","author":"Graves Alex","year":"2013"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"e_1_3_2_1_14_1","volume-title":"Long short-term memory. Neural computation","author":"Hochreiter Sepp","year":"1997"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2003.09.015"},{"key":"e_1_3_2_1_16_1","volume":"201","author":"Hong Tao","journal-title":"Rob J Hyndman."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.450"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298932"},{"key":"e_1_3_2_1_19_1","volume-title":"International Conference on Learning Representations .","author":"Kingma Diederik P","year":"2015"},{"key":"e_1_3_2_1_20_1","volume-title":"Regression quantiles. Econometrica: journal of the Econometric Society","author":"Koenker Roger","year":"1978"},{"key":"e_1_3_2_1_21_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 .   Alex Krizhevsky Ilya Sutskever and Geoffrey E Hinton. 2012. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems ."},{"key":"e_1_3_2_1_22_1","volume-title":"A review of unsupervised feature learning and deep learning for time-series modeling. Pattern Recognition Letters","author":"Martin","year":"2014"},{"key":"e_1_3_2_1_23_1","volume-title":"Visualizing data using t-SNE. Journal of Machine Learning Research","author":"van der Maaten Laurens","year":"2008"},{"key":"e_1_3_2_1_24_1","unstructured":"Tomas Mikolov Ilya Sutskever Kai Chen Greg S Corrado and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems .   Tomas Mikolov Ilya Sutskever Kai Chen Greg S Corrado and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems ."},{"key":"e_1_3_2_1_25_1","volume-title":"Wavenet: A generative model for raw audio. arXiv preprint arXiv:1609.03499","author":"van den Oord Aaron","year":"2016"},{"key":"e_1_3_2_1_26_1","unstructured":"Adam Paszke Sam Gross Soumith Chintala Gregory Chanan Edward Yang Zachary DeVito Zeming Lin Alban Desmaison Luca Antiga and Adam Lerer. 2017. Automatic differentiation in PyTorch. (2017).  Adam Paszke Sam Gross Soumith Chintala Gregory Chanan Edward Yang Zachary DeVito Zeming Lin Alban Desmaison Luca Antiga and Adam Lerer. 2017. Automatic differentiation in PyTorch. (2017)."},{"key":"e_1_3_2_1_27_1","unstructured":"Ilya Sutskever Oriol Vinyals and Quoc V Le. 2014. Sequence to sequence learning with neural networks. In Advances in Neural Information Processing Systems .   Ilya Sutskever Oriol Vinyals and Quoc V Le. 2014. Sequence to sequence learning with neural networks. In Advances in Neural Information Processing Systems ."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2015.2411629"},{"key":"e_1_3_2_1_29_1","volume-title":"A Multi-Horizon Quantile Recurrent Forecaster. arXiv preprint arXiv:1711.11053","author":"Wen Ruofeng","year":"2017"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.6.3.324"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2016.08.003"},{"key":"e_1_3_2_1_32_1","unstructured":"Hsiang-Fu Yu Nikhil Rao and Inderjit S Dhillon. 2016. Temporal regularized matrix factorization for high-dimensional time series prediction. In Advances in Neural Information Processing Systems .   Hsiang-Fu Yu Nikhil Rao and Inderjit S Dhillon. 2016. Temporal regularized matrix factorization for high-dimensional time series prediction. In Advances in Neural Information Processing Systems ."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.5555\/1927099.1927107"}],"event":{"name":"KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Anchorage AK USA","acronym":"KDD '19","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3292500.3330662","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3292500.3330662","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T01:02:09Z","timestamp":1750208529000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3292500.3330662"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,25]]},"references-count":33,"alternative-id":["10.1145\/3292500.3330662","10.1145\/3292500"],"URL":"https:\/\/doi.org\/10.1145\/3292500.3330662","relation":{},"subject":[],"published":{"date-parts":[[2019,7,25]]},"assertion":[{"value":"2019-07-25","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}