Knowledge-driven stock trend prediction and explanation via temporal convolutional network
MetadataShow full item record
Deng , S , Zhang , N , Zhang , W , Chen , J , Pan , J Z & Chen , H 2019 , Knowledge-driven stock trend prediction and explanation via temporal convolutional network . in L Liu & R White (eds) , Companion Proceedings of the 2019 World Wide Web Conference (WWW ’19 Companion) . Association for Computing Machinery, Inc , New York , pp. 678-685 , 2019 World Wide Web Conference, WWW 2019 , San Francisco , United States , 13/05/19 . https://doi.org/10.1145/3308560.3317701conference
© 2019 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC-BY 4.0 License. This paper is published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license. Authors reserve their rights to disseminate the work on their personal and corporate Web sites with the appropriate attribution. https://creativecommons.org/licenses/by/4.0/
Showing items related by title, author, creator and subject.
Predictability's aftermath : Downstream consequences of word predictability as revealed by repetition effects Rommers, Joost; Federmeier, Kara D. (2018-04)
Simple prediction scores predict good and devastating outcomes after stroke more accurately than physicians Reid, John Michael; Dai, Dingwei; Delmonte, Susanna; Counsell, Carl; Phillips, Stephen J.; Macleod, Mary Joan (2017-05)
22 Years of predictive testing for Huntington’s disease : the experience of the UK Huntington’s Prediction Consortium Baig, Sheharyar S; Strong, Mark; Rosser, Elisabeth; Taverner, Nicola V; Glew, Ruth; Miedzybrodzka, Zofia Helena; Clarke, Angus; Craufurd, David; Quarrell, Oliver W (2016-10)