{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T05:06:11Z","timestamp":1750309571016,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":11,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,11,15]],"date-time":"2024-11-15T00:00:00Z","timestamp":1731628800000},"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":[[2024,11,15]]},"DOI":"10.1145\/3718751.3718892","type":"proceedings-article","created":{"date-parts":[[2025,4,28]],"date-time":"2025-04-28T06:29:37Z","timestamp":1745821777000},"page":"866-870","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Stock Return Prediction Using Hybrid Machine Learning Method Based on Optiver Company Data"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7363-0658","authenticated-orcid":false,"given":"Jiahua","family":"Yang","sequence":"first","affiliation":[{"name":"Australian National University, Canberra, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0361-9701","authenticated-orcid":false,"given":"Yutao","family":"Guo","sequence":"additional","affiliation":[{"name":"The University of HongKong, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-2768-5334","authenticated-orcid":false,"given":"Lingdi","family":"Meng","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5002-9878","authenticated-orcid":false,"given":"Yi","family":"Zhu","sequence":"additional","affiliation":[{"name":"Australian National University, Canberra, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8218-2124","authenticated-orcid":false,"given":"Weiyi","family":"Geng","sequence":"additional","affiliation":[{"name":"Australian National University, Canberra, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,4,27]]},"reference":[{"key":"e_1_3_3_1_1_2","volume-title":"A review of ARIMA vs. machine learning approaches for time series forecasting in data driven networks.\u00a0Future Internet,\u00a015(8), 255","author":"Kontopoulou V. I.","year":"2023","unstructured":"Kontopoulou, V. I., Panagopoulos, A. D., Kakkos, I., & Matsopoulos, G. K. (2023). A review of ARIMA vs. machine learning approaches for time series forecasting in data driven networks.\u00a0Future Internet,\u00a015(8), 255."},{"key":"e_1_3_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.2307\/1912773"},{"key":"e_1_3_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.3390\/en14206782"},{"key":"e_1_3_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2021.126266"},{"key":"e_1_3_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2022.04.058"},{"key":"e_1_3_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.techfore.2021.120658"},{"key":"e_1_3_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1080\/10494820.2021.1928235"},{"key":"e_1_3_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2014.07.040"},{"key":"e_1_3_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.3390\/data7050051"},{"key":"e_1_3_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1093\/rfs\/hhaa009"},{"key":"e_1_3_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-45014-9_1"}],"event":{"name":"ICBAR 2024: 2024 4th International Conference on Big Data, Artificial Intelligence and Risk Management","acronym":"ICBAR 2024","location":"Chengdu Guangdong China"},"container-title":["Proceedings of the 2024 4th International Conference on Big Data, Artificial Intelligence and Risk Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3718751.3718892","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3718751.3718892","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:19:13Z","timestamp":1750295953000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3718751.3718892"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,15]]},"references-count":11,"alternative-id":["10.1145\/3718751.3718892","10.1145\/3718751"],"URL":"https:\/\/doi.org\/10.1145\/3718751.3718892","relation":{},"subject":[],"published":{"date-parts":[[2024,11,15]]},"assertion":[{"value":"2025-04-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}