{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T01:09:14Z","timestamp":1743037754918,"version":"3.40.3"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030152345"},{"type":"electronic","value":"9783030152352"}],"license":[{"start":{"date-parts":[[2019,4,25]],"date-time":"2019-04-25T00:00:00Z","timestamp":1556150400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-15235-2_134","type":"book-chapter","created":{"date-parts":[[2019,4,24]],"date-time":"2019-04-24T13:02:48Z","timestamp":1556110968000},"page":"1007-1014","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["The Impact Factors of Neural Network Based Time Series Prediction: Taking Stock Price as an Example"],"prefix":"10.1007","author":[{"given":"Yue","family":"Hou","sequence":"first","affiliation":[]},{"given":"Heng","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Bin","family":"Xie","sequence":"additional","affiliation":[]},{"given":"Feng","family":"Ding","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,4,25]]},"reference":[{"issue":"1","key":"134_CR1","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1007\/BF02532251","volume":"21","author":"H Akaike","year":"1969","unstructured":"Akaike H (1969) Fitting autoregressive models for prediction. Ann Inst Stat Math 21(1):243\u2013247","journal-title":"Ann Inst Stat Math"},{"key":"134_CR2","volume-title":"Time Series Analysis: Forecasting and Control","author":"GEP Box","year":"2015","unstructured":"Box GEP, Jenkins GM, Reinsel GC et al (2015) Time Series Analysis: Forecasting and Control. Wiley, Hoboken"},{"issue":"1","key":"134_CR3","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1111\/j.1467-9892.1980.tb00297.x","volume":"1","author":"CWJ Granger","year":"1980","unstructured":"Granger CWJ, Joyeux R (1980) An introduction to long-memory time series models and fractional differencing. J Time Ser Anal 1(1):15\u201329","journal-title":"J Time Ser Anal"},{"key":"134_CR4","unstructured":"Varfis A, Versino C (1990) Univariate economic time series forecasting by connectionist methods. In: Proceedings of INNC, vol 90, pp 342\u2013345"},{"key":"134_CR5","unstructured":"Liu X (2016) Research on the mothed of forecasting stock price based on the BP neural network: taking military industry as an example. Beijing Jiaotong university, Beijing"},{"key":"134_CR6","unstructured":"Xiao Q (2017) The research and application of artificial neural networks in stock price forecasting. South China University of Technology, Guangzhou"},{"issue":"5","key":"134_CR7","first-page":"89","volume":"31","author":"YY Chen","year":"2014","unstructured":"Chen YY, Zhang ZX, Li WB (2014) Neural network based stock price prediction model. Comput Appl Softw 31(5):89\u201392","journal-title":"Comput Appl Softw"},{"key":"134_CR8","unstructured":"Han L (2016) Research on the stock prediction based on LM-BP neural networks. Northeast Agricultural University, Harbin"},{"key":"134_CR9","first-page":"010","volume":"1","author":"XY Liu","year":"2018","unstructured":"Liu XY (2018) Time series prediction and its application. Financ Theory Teach 1:010","journal-title":"Financ Theory Teach"},{"issue":"01","key":"134_CR10","first-page":"152","volume":"27","author":"JF Guo","year":"2017","unstructured":"Guo JF, Li Y, An D (2017) Prediction for short-term sock price based on LM-GA-BP neural network. Comput Technol Dev 27(01):152\u2013155+15","journal-title":"Comput Technol Dev"},{"issue":"8","key":"134_CR11","first-page":"226","volume":"25","author":"H Yang","year":"2017","unstructured":"Yang H, Ma JH (2017) Research on method of regularization parameter solution. Comput Measure Control 25(8):226\u2013229","journal-title":"Comput Measure Control"},{"key":"134_CR12","unstructured":"Jiao RQ (2017) Regularization parameters selection of Bayesian penalized regression. Southwest Jiaotong University, Chengdu"},{"issue":"12","key":"134_CR13","first-page":"2270","volume":"37","author":"L Wang","year":"2015","unstructured":"Wang L, Peng L, Xia D, Zeng Y (2015) BP neural network incorporating self-adaptive differential evolution algorithm for time series forecasting. Comput Eng Sci 37(12):2270\u20132275","journal-title":"Comput Eng Sci"},{"issue":"99","key":"134_CR14","first-page":"1","volume":"PP","author":"JS Chou","year":"2018","unstructured":"Chou JS, Nguyen TK (2018) Forward forecast of stock price using sliding-window metaheuristic-optimized machine learning regression. IEEE Trans Ind Inf PP(99):1","journal-title":"IEEE Trans Ind Inf"},{"issue":"5","key":"134_CR15","first-page":"29","volume":"25","author":"RH Cai","year":"2017","unstructured":"Cai RH, Cui YX, Xue P (2017) Research on the methods of determining the number of hidden nodes in three layer BP neural network. Comput Inf Technol 25(5):29\u201333","journal-title":"Comput Inf Technol"},{"key":"134_CR16","volume-title":"Neural Networks and Deep Learning","author":"MA Nielsen","year":"2015","unstructured":"Nielsen MA (2015) Neural Networks and Deep Learning. Determination press, USA"}],"container-title":["Advances in Intelligent Systems and Computing","Cyber Security Intelligence and Analytics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-15235-2_134","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,18]],"date-time":"2019-05-18T03:15:28Z","timestamp":1558149328000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-15235-2_134"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4,25]]},"ISBN":["9783030152345","9783030152352"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-15235-2_134","relation":{},"ISSN":["2194-5357","2194-5365"],"issn-type":[{"type":"print","value":"2194-5357"},{"type":"electronic","value":"2194-5365"}],"subject":[],"published":{"date-parts":[[2019,4,25]]},"assertion":[{"value":"25 April 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CSIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"The International Conference on Cyber Security Intelligence and Analytics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenyang","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 February 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 February 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"csia2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/csia2019.csp.escience.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}