Comparative Study of Real Time Machine Learning Models for Stock Prediction Through Streaming Data

dc.authorid Misra, Sanjay/0000-0002-3556-9331
dc.authorid BEHERA, RANJAN KUMAR/0000-0001-9267-3621
dc.authorscopusid 55185224200
dc.authorscopusid 57202709889
dc.authorscopusid 55428272300
dc.authorscopusid 56962766700
dc.authorscopusid 6603451290
dc.authorwosid BEHERA, RANJAN/I-2680-2017
dc.authorwosid Damaševičius, Robertas/E-1387-2017
dc.authorwosid Rath, Santanu/O-6685-2017
dc.authorwosid Misra, Sanjay/K-2203-2014
dc.contributor.author Behera, Ranjan Kumar
dc.contributor.author Das, Sushree
dc.contributor.author Rath, Santanu Kumar
dc.contributor.author Misra, Sanjay
dc.contributor.author Damasevicius, Robertas
dc.contributor.other Computer Engineering
dc.contributor.other Computer Engineering
dc.date.accessioned 2024-10-06T10:58:12Z
dc.date.available 2024-10-06T10:58:12Z
dc.date.issued 2020
dc.department Atılım University en_US
dc.department-temp [Behera, Ranjan Kumar; Das, Sushree; Rath, Santanu Kumar] Natl Inst Technol Rourkela, Rourkela, India; [Misra, Sanjay] Atilim Univ, Ankara, Turkey; [Misra, Sanjay] Covenant Univ, Ota, Nigeria; [Damasevicius, Robertas] Vytautas Magnus Univ, Kaunas, Lithuania en_US
dc.description Misra, Sanjay/0000-0002-3556-9331; BEHERA, RANJAN KUMAR/0000-0001-9267-3621 en_US
dc.description.abstract Stock prediction is one of the emerging applications in the field of data science which help the companies to make better decision strategy. Machine learning models play a vital role in the field of prediction. In this paper, we have proposed various machine learning models which predicts the stock price from the real-time streaming data. Streaming data has been a potential source for real-time prediction which deals with continuous flow of data having information from various sources like social networking websites, server logs, mobile phone applications, trading floors etc. We have adopted the distributed platform, Spark to analyze the streaming data collected from two different sources as represented in two case studies in this paper. The first case study is based on stock prediction from the historical data collected from Google finance websites through NodeJs and the second one is based on the sentiment analysis of Twitter collected through Twitter API available in Stanford NLP package. Several researches have been made in developing models for stock prediction based on static data. In this work, an effort has been made to develop scalable, fault tolerant models for stock prediction from the real-time streaming data. The Proposed model is based on a distributed architecture known as Lambda architecture. The extensive comparison is made between actual and predicted output for different machine learning models. Support vector regression is found to have better accuracy as compared to other models. The historical data is considered as a ground truth data for validation. en_US
dc.description.sponsorship Fund for Improvement of S&T Infrastructure in Universities and Higher Educational Institutions (FIST) Scheme under Department of Science and Technology (DST), Govt. of India; department of computer science & engineering, National Institute of Technology, Rourkela, India en_US
dc.description.sponsorship This research work was supported by Fund for Improvement of S&T Infrastructure in Universities and Higher Educational Institutions (FIST) Scheme under Department of Science and Technology (DST), Govt. of India The authors wish to express their gratitude and heartiest thanks to the department of computer science & engineering, National Institute of Technology, Rourkela, India for providing their research support. en_US
dc.description.woscitationindex Science Citation Index Expanded
dc.identifier.citationcount 12
dc.identifier.endpage 1147 en_US
dc.identifier.issn 0948-695X
dc.identifier.issn 0948-6968
dc.identifier.issue 9 en_US
dc.identifier.scopus 2-s2.0-85112043071
dc.identifier.scopusquality Q3
dc.identifier.startpage 1128 en_US
dc.identifier.uri https://hdl.handle.net/20.500.14411/8871
dc.identifier.volume 26 en_US
dc.identifier.wos WOS:000596748600003
dc.identifier.wosquality Q4
dc.institutionauthor Mısra, Sanjay
dc.institutionauthor Mısra, Sanjay
dc.language.iso en en_US
dc.publisher Graz Univ Technolgoy, inst information Systems Computer Media-iicm en_US
dc.relation.ispartof Journal of Universal Computer Science en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.scopus.citedbyCount 23
dc.subject Spark Streaming en_US
dc.subject NodeJS en_US
dc.subject Twitter API en_US
dc.subject Lambda Architecture en_US
dc.subject MLlib en_US
dc.title Comparative Study of Real Time Machine Learning Models for Stock Prediction Through Streaming Data en_US
dc.type Article en_US
dc.wos.citedbyCount 15
dspace.entity.type Publication
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relation.isAuthorOfPublication.latestForDiscovery 53e88841-fdb7-484f-9e08-efa4e6d1a090
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