{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T02:56:04Z","timestamp":1771901764884,"version":"3.50.1"},"reference-count":22,"publisher":"Wiley","license":[{"start":{"date-parts":[[2020,6,9]],"date-time":"2020-06-09T00:00:00Z","timestamp":1591660800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["11801060"],"award-info":[{"award-number":["11801060"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["SDKDYC190114"],"award-info":[{"award-number":["SDKDYC190114"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004295","name":"Shandong University of Science and Technology","doi-asserted-by":"publisher","award":["11801060"],"award-info":[{"award-number":["11801060"]}],"id":[{"id":"10.13039\/501100004295","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004295","name":"Shandong University of Science and Technology","doi-asserted-by":"publisher","award":["SDKDYC190114"],"award-info":[{"award-number":["SDKDYC190114"]}],"id":[{"id":"10.13039\/501100004295","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2020,6,9]]},"abstract":"<jats:p>Integrating autoencoder (AE), long short-term memory (LSTM), and convolutional neural network (CNN), we propose an interpretable deep learning architecture for Granger causality inference, named deep learning-based Granger causality inference (DLI). Two contributions of the proposed DLI are to reveal the Granger causality between the bitcoin price and S&amp;P index and to forecast the bitcoin price and S&amp;P index with a higher accuracy. Experimental results demonstrate that there is a bidirectional but asymmetric Granger causality between the bitcoin price and S&amp;P index. And the DLI performs a superior prediction accuracy by integrating variables that have causalities with the target variable into the prediction process.<\/jats:p>","DOI":"10.1155\/2020\/5960171","type":"journal-article","created":{"date-parts":[[2020,6,9]],"date-time":"2020-06-09T23:31:21Z","timestamp":1591745481000},"page":"1-6","source":"Crossref","is-referenced-by-count":11,"title":["DLI: A Deep Learning-Based Granger Causality Inference"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1238-6604","authenticated-orcid":true,"given":"Wei","family":"Peng","sequence":"first","affiliation":[{"name":"College of Economics and Management, Shandong University of Science and Technology, Qingdao 266590, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmse.2019.05.003"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.05.023"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2020.03.257"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1057\/jam.2015.5"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmse.2019.09.001"},{"key":"6","first-page":"31","volume-title":"Chapter 2-is bitcoin a real currency? an economic appraisal","year":"2015"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1016\/j.econlet.2018.07.031"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1016\/j.frl.2015.10.025"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.2307\/1912791"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1016\/j.econmod.2017.12.004"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1016\/j.resconrec.2016.09.032"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1016\/j.pnucene.2014.07.006"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2012.09.019"},{"key":"14","doi-asserted-by":"publisher","DOI":"10.2307\/2286348"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1016\/0304-4076(94)01616-8"},{"key":"16","doi-asserted-by":"publisher","DOI":"10.1080\/00036840500405763"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2017.04.030"},{"key":"18","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmse.2020.01.002"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2018.10.034"},{"key":"20","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2018.11.014"},{"key":"21","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/6082047"},{"issue":"10","key":"22","volume":"3361","year":"1995","journal-title":"The Handbook of Brain Theory and Neural Networks"}],"container-title":["Complexity"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2020\/5960171.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2020\/5960171.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2020\/5960171.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,6,9]],"date-time":"2020-06-09T23:31:23Z","timestamp":1591745483000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/complexity\/2020\/5960171\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,9]]},"references-count":22,"alternative-id":["5960171","5960171"],"URL":"https:\/\/doi.org\/10.1155\/2020\/5960171","relation":{},"ISSN":["1076-2787","1099-0526"],"issn-type":[{"value":"1076-2787","type":"print"},{"value":"1099-0526","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,9]]}}}