{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T23:18:56Z","timestamp":1777591136556,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":20,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,10,19]],"date-time":"2020-10-19T00:00:00Z","timestamp":1603065600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Science Foundation","award":["1755464, 1723250"],"award-info":[{"award-number":["1755464, 1723250"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,10,19]]},"DOI":"10.1145\/3340531.3412738","type":"proceedings-article","created":{"date-parts":[[2020,10,19]],"date-time":"2020-10-19T06:18:51Z","timestamp":1603088331000},"page":"2485-2492","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":20,"title":["Price Forecast with High-Frequency Finance Data"],"prefix":"10.1145","author":[{"given":"Yuechun","family":"Gu","sequence":"first","affiliation":[{"name":"University of Electronic Science and Technology of China, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Da","family":"Yan","sequence":"additional","affiliation":[{"name":"University of Alabama at Birmingham, Birmingham, AL, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sibo","family":"Yan","sequence":"additional","affiliation":[{"name":"Freddie Mac, Washington, DC, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhe","family":"Jiang","sequence":"additional","affiliation":[{"name":"University of Alabama, Tuscaloosa, AL, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,10,19]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Jamie Ryan Kiros, and Geoffrey E. Hinton. Layer normalization. CoRR, abs\/1607.06450","author":"Ba Lei Jimmy","year":"2016","unstructured":"Lei Jimmy Ba , Jamie Ryan Kiros, and Geoffrey E. Hinton. Layer normalization. CoRR, abs\/1607.06450 , 2016 . Lei Jimmy Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton. Layer normalization. CoRR, abs\/1607.06450, 2016."},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0180944"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0304-4076(00)00052-X"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/for.2585"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/W14-4012"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.2307\/2951767"},{"key":"e_1_3_2_2_7_1","first-page":"3108822","article-title":"Estimating a local heston model","author":"Ellickson Bryan","year":"2018","unstructured":"Bryan Ellickson , Miao Sun , Duke Whang , and Sibo Yan . Estimating a local heston model . SSRN 3108822 , 2018 . Bryan Ellickson, Miao Sun, Duke Whang, and Sibo Yan. Estimating a local heston model. SSRN 3108822, 2018.","journal-title":"SSRN"},{"key":"e_1_3_2_2_8_1","volume-title":"Deepar: Probabilistic forecasting with autoregressive recurrent networks. CoRR, abs\/1704.04110","author":"Flunkert Valentin","year":"2017","unstructured":"Valentin Flunkert , David Salinas , and Jan Gasthaus . Deepar: Probabilistic forecasting with autoregressive recurrent networks. CoRR, abs\/1704.04110 , 2017 . Valentin Flunkert, David Salinas, and Jan Gasthaus. Deepar: Probabilistic forecasting with autoregressive recurrent networks. CoRR, abs\/1704.04110, 2017."},{"key":"e_1_3_2_2_9_1","first-page":"1901","volume-title":"AISTATS","author":"Gasthaus Jan","year":"2019","unstructured":"Jan Gasthaus , Konstantinos Benidis , Yuyang Wang , Syama Sundar Rangapuram , David Salinas , Valentin Flunkert , and Tim Januschowski . Probabilistic forecasting with spline quantile function rnns . In AISTATS , pages 1901 -- 1910 , 2019 . Jan Gasthaus, Konstantinos Benidis, Yuyang Wang, Syama Sundar Rangapuram, David Salinas, Valentin Flunkert, and Tim Januschowski. Probabilistic forecasting with spline quantile function rnns. In AISTATS, pages 1901--1910, 2019."},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_2_2_11_1","first-page":"448","volume-title":"Proceedings of the 32nd International Conference on Machine Learning, ICML 2015","author":"Ioffe Sergey","year":"2015","unstructured":"Sergey Ioffe and Christian Szegedy . Batch normalization : Accelerating deep network training by reducing internal covariate shift . In Proceedings of the 32nd International Conference on Machine Learning, ICML 2015 , Lille, France , 6-11 July 2015 , pages 448 -- 456 , 2015. Sergey Ioffe and Christian Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, Lille, France, 6-11 July 2015, pages 448--456, 2015."},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0212320"},{"key":"e_1_3_2_2_13_1","volume-title":"3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings","author":"Diederik","year":"2015","unstructured":"Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization . In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings , 2015 . Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, 2015."},{"key":"e_1_3_2_2_14_1","volume-title":"Stock prediction using twitter sentiment analysis","author":"Mittal Anshul","year":"2012","unstructured":"Anshul Mittal and Arpit Goel . Stock prediction using twitter sentiment analysis . Standford University , CS229, 15, 2012 . Anshul Mittal and Arpit Goel. Stock prediction using twitter sentiment analysis. Standford University, CS229, 15, 2012."},{"key":"e_1_3_2_2_15_1","first-page":"1419","volume-title":"IJCNN","author":"Nelson David M. Q.","year":"2017","unstructured":"David M. Q. Nelson , Adriano C. M. Pereira , and Renato A . de Oliveira. Stock market's price movement prediction with LSTM neural networks . In IJCNN , pages 1419 -- 1426 , 2017 . David M. Q. Nelson, Adriano C. M. Pereira, and Renato A. de Oliveira. Stock market's price movement prediction with LSTM neural networks. In IJCNN, pages 1419--1426, 2017."},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/IRI.2019.00018"},{"key":"e_1_3_2_2_17_1","first-page":"7796","volume-title":"NeurIPS","author":"Rangapuram Syama Sundar","year":"2018","unstructured":"Syama Sundar Rangapuram , Matthias W. Seeger , Jan Gasthaus , Lorenzo Stella , Yuyang Wang , and Tim Januschowski . Deep state space models for time series forecasting . In NeurIPS , pages 7796 -- 7805 , 2018 . Syama Sundar Rangapuram, Matthias W. Seeger, Jan Gasthaus, Lorenzo Stella, Yuyang Wang, and Tim Januschowski. Deep state space models for time series forecasting. In NeurIPS, pages 7796--7805, 2018."},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2019.122343"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214505000000169"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2019.2907260"}],"event":{"name":"CIKM '20: The 29th ACM International Conference on Information and Knowledge Management","location":"Virtual Event Ireland","acronym":"CIKM '20","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 29th ACM International Conference on Information & Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3340531.3412738","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3340531.3412738","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:02:55Z","timestamp":1750197775000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3340531.3412738"}},"subtitle":["An Autoregressive Recurrent Neural Network Model with Technical Indicators"],"short-title":[],"issued":{"date-parts":[[2020,10,19]]},"references-count":20,"alternative-id":["10.1145\/3340531.3412738","10.1145\/3340531"],"URL":"https:\/\/doi.org\/10.1145\/3340531.3412738","relation":{},"subject":[],"published":{"date-parts":[[2020,10,19]]},"assertion":[{"value":"2020-10-19","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}