{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T21:19:50Z","timestamp":1775510390990,"version":"3.50.1"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T00:00:00Z","timestamp":1719532800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T00:00:00Z","timestamp":1719532800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"DOI":"10.1007\/s42979-024-02916-z","type":"journal-article","created":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T20:28:26Z","timestamp":1719606506000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Novel Approach for Forecasting Price of Stock Market using Machine Learning Techniques"],"prefix":"10.1007","volume":"5","author":[{"given":"Abhinay","family":"Yadav","sequence":"first","affiliation":[]},{"given":"Vineet","family":"Kumar","sequence":"additional","affiliation":[]},{"given":"Satyendra","family":"Singh","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7532-5585","authenticated-orcid":false,"given":"Ashish Kumar","family":"Mishra","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,6,28]]},"reference":[{"issue":"10","key":"2916_CR1","doi-asserted-by":"publisher","first-page":"2222","DOI":"10.1109\/TNNLS.2016.2582924","volume":"28","author":"K Greff","year":"2016","unstructured":"Greff K, Srivastava RK, Koutn\u00edk J, Steunebrink BR, Schmidhuber J. LSTM: a search space odyssey. IEEE Trans Neural Netw Learn Syst. 2016;28(10):2222\u201332.","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"10","key":"2916_CR2","first-page":"1033","volume":"5","author":"VKS Reddy","year":"2018","unstructured":"Reddy VKS. Stock market prediction using machine learning. Int Res J Eng Technol (IRJET). 2018;5(10):1033\u20135.","journal-title":"Int Res J Eng Technol (IRJET)"},{"key":"2916_CR3","doi-asserted-by":"crossref","unstructured":"Wang H. Stock price prediction based on machine learning approaches. In: Proceedings of the 3rd international conference on data science and information technology. 2020. p. 1\u20135.","DOI":"10.1145\/3414274.3414275"},{"key":"2916_CR4","doi-asserted-by":"crossref","unstructured":"Adhikar AJ, Jadhav AK, KH CG, HS MS. Literature survey on stock price prediction using machine learning. Int J Eng Appl Sci Technol. 2020;5(8):2143\u2013455.","DOI":"10.33564\/IJEAST.2020.v05i08.040"},{"key":"2916_CR5","doi-asserted-by":"crossref","unstructured":"Kadam MY, Kulkarni MS, Lonsane, MS, Khandagale AS. A survey on stock market price prediction system using machine learning techniques. 2022.","DOI":"10.22214\/ijraset.2022.40635"},{"key":"2916_CR6","doi-asserted-by":"crossref","unstructured":"Torres PEP, Hern\u00e1ndez-\u00c1lvarez M, Torres Hern\u00e1ndez EA, Yoo SG. Stock market data prediction using machine learning techniques. In: Information technology and systems: proceedings of ICITS 2019. Springer International Publishing; 2019. p. 539\u201347.","DOI":"10.1007\/978-3-030-11890-7_52"},{"issue":"4","key":"2916_CR7","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1002\/isaf.1459","volume":"26","author":"M Nikou","year":"2019","unstructured":"Nikou M, Mansourfar G, Bagherzadeh J. Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms. Intell Syst Account Finance Manag. 2019;26(4):164\u201374.","journal-title":"Intell Syst Account Finance Manag"},{"key":"2916_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.114332","volume":"169","author":"H Rezaei","year":"2021","unstructured":"Rezaei H, Faaljou H, Mansourfar G. Stock price prediction using deep learning and frequency decomposition. Expert Syst Appl. 2021;169: 114332.","journal-title":"Expert Syst Appl"},{"key":"2916_CR9","doi-asserted-by":"crossref","unstructured":"Karim ME, Foysal M, Das S. Stock price prediction using Bi-LSTM and GRU-based hybrid deep learning approach. In: Proceedings of third doctoral symposium on computational intelligence: DoSCI 2022. Singapore: Springer Nature Singapore; 2022. p. 701\u201311.","DOI":"10.1007\/978-981-19-3148-2_60"},{"key":"2916_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.114800","volume":"177","author":"A Thakkar","year":"2021","unstructured":"Thakkar A, Chaudhari K. A comprehensive survey on deep neural networks for stock market: the need, challenges, and future directions. Expert Syst Appl. 2021;177: 114800.","journal-title":"Expert Syst Appl"},{"key":"2916_CR11","doi-asserted-by":"crossref","unstructured":"Hossain MA, Karim R, Thulasiram R, Bruce ND, Wang Y. Hybrid deep learning model for stock price prediction. In: 2018 IEEE symposium series on computational intelligence (ssci). IEEE; 2018. p. 1837\u201344.","DOI":"10.1109\/SSCI.2018.8628641"},{"key":"2916_CR12","doi-asserted-by":"crossref","unstructured":"Babu CN, Reddy BE. Selected Indian stock predictions using a hybrid ARIMA-GARCH model. In: 2014 international conference on advances in electronics computers and communications. IEEE; 2014. p. 1\u20136.","DOI":"10.1109\/ICAECC.2014.7002382"},{"key":"2916_CR13","doi-asserted-by":"crossref","unstructured":"Vanipriya CH, Thammi Reddy K. Indian stock market predictor system. In: ICT and critical infrastructure: proceedings of the 48th annual convention of Computer Society of India-Vol II: hosted by CSI Vishakapatnam Chapter. Springer International Publishing; 2014. p. 17\u201326.","DOI":"10.1007\/978-3-319-03095-1_3"},{"key":"2916_CR14","doi-asserted-by":"publisher","first-page":"71326","DOI":"10.1109\/ACCESS.2020.2985763","volume":"8","author":"AH Bukhari","year":"2020","unstructured":"Bukhari AH, Raja MAZ, Sulaiman M, Islam S, Shoaib M, Kumam P. Fractional neuro-sequential ARFIMA-LSTM for financial market forecasting. IEEE Access. 2020;8:71326\u201338.","journal-title":"IEEE Access"},{"key":"2916_CR15","first-page":"1","volume":"2021","author":"Y Gao","year":"2021","unstructured":"Gao Y, Wang R, Zhou E. Stock prediction based on optimized LSTM and GRU models. Sci Progr. 2021;2021:1\u20138.","journal-title":"Sci Progr"},{"key":"2916_CR16","doi-asserted-by":"crossref","unstructured":"Koukaras P, Nousi C, Tjortjis C. Stock market prediction using microblogging sentiment analysis and machine learning. In: Telecom, vol. 3, no. 2. MDPI; 2022. p. 358\u201378.","DOI":"10.3390\/telecom3020019"},{"issue":"1","key":"2916_CR17","first-page":"3","volume":"160","author":"SB Kotsiantis","year":"2007","unstructured":"Kotsiantis SB, Zaharakis I, Pintelas P. Supervised machine learning: a review of classification techniques. Emerg Artif Intell Appl Comput Eng. 2007;160(1):3\u201324.","journal-title":"Emerg Artif Intell Appl Comput Eng"},{"issue":"4","key":"2916_CR18","first-page":"25","volume":"8","author":"KH Sadia","year":"2019","unstructured":"Sadia KH, Sharma A, Paul A, Padhi S, Sanyal S. Stock market prediction using machine learning algorithms. Int J Eng Adv Technol. 2019;8(4):25\u201331.","journal-title":"Int J Eng Adv Technol"},{"key":"2916_CR19","unstructured":"Jakub A. Make kNN 300 times faster than Scikit-learn\u2019s in 20 lines! towardsdatascience.com. 2020. https:\/\/towardsdatascience.com\/make-knn-300-times-faster-than-scikit-learns-in-20-lines-5e29d74e76bb. Accessed 30 Oct 2022."},{"key":"2916_CR20","doi-asserted-by":"crossref","unstructured":"Huynh HD, Dang LM, Duong D. A new model for stock price movements prediction using deep neural network. In: Proceedings of the 8th international symposium on information and communication technology. 2017. p. 57\u201362.","DOI":"10.1145\/3155133.3155202"},{"key":"2916_CR21","doi-asserted-by":"crossref","unstructured":"Kukreti V, Bhatt C, Dani R. A stock market trends analysis of reliance using machine learning techniques. In: 2023 6th International Conference on Information Systems and Computer Networks (ISCON). IEEE; 2023. p. 1\u20136.","DOI":"10.1109\/ISCON57294.2023.10112145"},{"key":"2916_CR22","unstructured":"Avramov D, Chordia T, Jostova G, Philipov A. Bonds, stocks, and sources of mispricing. George Mason University School of Business Research paper. 2019. p. 18\u20135."},{"issue":"1","key":"2916_CR23","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0227222","volume":"15","author":"J Qiu","year":"2020","unstructured":"Qiu J, Wang B, Zhou C. Forecasting stock prices with long-short term memory neural network based on attention mechanism. PLoS One. 2020;15(1): e0227222.","journal-title":"PLoS One."},{"key":"2916_CR24","doi-asserted-by":"crossref","unstructured":"Nelson DM, Pereira AC, De Oliveira RA. Stock market\u2019s price movement prediction with LSTM neural networks. In: 2017 International joint conference on neural networks (IJCNN). IEEE; 2017. p. 1419\u201326.","DOI":"10.1109\/IJCNN.2017.7966019"},{"key":"2916_CR25","doi-asserted-by":"crossref","unstructured":"Budhani N, Jha CK, Budhani SK. Prediction of stock market using artificial neural network. In: 2014 international conference of soft computing techniques for engineering and technology (ICSCTET). IEEE; 2014. p. 1\u20138.","DOI":"10.1109\/ICSCTET.2015.7371196"},{"key":"2916_CR26","unstructured":"Recurrent neural networks. Research Gate. 2019. Accessed 30 Oct 2022."},{"issue":"21","key":"2916_CR27","doi-asserted-by":"publisher","first-page":"2717","DOI":"10.3390\/electronics10212717","volume":"10","author":"N Rouf","year":"2021","unstructured":"Rouf N, Malik MB, Arif T, Sharma S, Singh S, Aich S, Kim HC. Stock market prediction using machine learning techniques: a decade survey on methodologies, recent developments, and future directions. Electronics. 2021;10(21):2717.","journal-title":"Electronics"},{"issue":"4","key":"2916_CR28","first-page":"97","volume":"8","author":"M Umer","year":"2019","unstructured":"Umer M, Awais M, Muzammul M. Stock market prediction using machine learning (ML) algorithms. ADCAIJ Adv Distrib Comput Artif Intell J. 2019;8(4):97\u2013116.","journal-title":"ADCAIJ Adv Distrib Comput Artif Intell J"},{"key":"2916_CR29","doi-asserted-by":"crossref","unstructured":"Selvin S, Vinayakumar R, Gopalakrishnan EA, Menon VK, Soman KP. Stock price prediction using LSTM, RNN and CNN-sliding window model. In: 2017 international conference on advances in computing, communications and informatics (icacci). IEEE; 2017. p. 1643\u201347.","DOI":"10.1109\/ICACCI.2017.8126078"},{"key":"2916_CR30","unstructured":"Chung J, Gulcehre C, Cho K, Bengio Y. Empirical evaluation of gated recurrent neural networks on sequence modeling. 2014. arXiv:1412.3555."},{"key":"2916_CR31","unstructured":"Jozefowicz R, Zaremba W, Sutskever I. An empirical exploration of recurrent network architectures. In: International conference on machine learning. PMLR; 2015. p. 2342\u201350."},{"key":"2916_CR32","doi-asserted-by":"crossref","unstructured":"Akita R, Yoshihara A, Matsubara T, Uehara K. Deep learning for stock prediction using numerical and textual information. In: 2016 IEEE\/ACIS 15th international conference on computer and information science (ICIS). IEEE; 2016. p. 1\u20136.","DOI":"10.1109\/ICIS.2016.7550882"},{"key":"2916_CR33","doi-asserted-by":"publisher","first-page":"55392","DOI":"10.1109\/ACCESS.2018.2868970","volume":"6","author":"DL Minh","year":"2018","unstructured":"Minh DL, Sadeghi-Niaraki A, Huy HD, Min K, Moon H. Deep learning approach for short-term stock trends prediction based on two-stream gated recurrent unit network. IEEE Access. 2018;6:55392\u2013404.","journal-title":"IEEE Access"},{"key":"2916_CR34","doi-asserted-by":"crossref","unstructured":"Althelaya KA, El-Alfy ESM, Mohammed S. Stock market forecast using multivariate analysis with bidirectional and stacked (LSTM, GRU). In: 2018 21st Saudi computer society national computer conference (NCC). IEEE; 2018. p. 1\u20137.","DOI":"10.1109\/NCG.2018.8593076"},{"issue":"10","key":"2916_CR35","doi-asserted-by":"publisher","first-page":"3702","DOI":"10.3390\/su10103702","volume":"10","author":"U Khan","year":"2018","unstructured":"Khan U, Aadil F, Ghazanfar MA, Khan S, Metawa N, Muhammad K, Nam Y. A robust regression-based stock exchange forecasting and determination of correlation between stock markets. Sustainability. 2018;10(10):3702.","journal-title":"Sustainability"},{"key":"2916_CR36","unstructured":"thingSpeakRead.Mathworks. (n.d.). https:\/\/www.mathworks.com\/help\/thingspeak\/calculate-simple-moving-average.html. Accessed 30 Oct 2022."},{"key":"2916_CR37","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-25388-6","volume-title":"Lectures on the nearest neighbor method","author":"G Biau","year":"2015","unstructured":"Biau G, Devroye L. Lectures on the nearest neighbor method, vol. 246. Cham: Springer International Publishing; 2015."},{"key":"2916_CR38","doi-asserted-by":"crossref","unstructured":"Pagolu VS, Reddy KN, Panda G, Majhi B. Sentiment analysis of Twitter data for predicting stock market movements. In: 2016 international conference on signal processing, communication, power and embedded system (SCOPES). IEEE; 2016. p. 1345\u201350.","DOI":"10.1109\/SCOPES.2016.7955659"},{"key":"2916_CR39","doi-asserted-by":"crossref","unstructured":"Khare K, Darekar O, Gupta P, Attar VZ. Short term stock price prediction using deep learning. In: 2017 2nd IEEE international conference on recent trends in electronics, information & communication technology (RTEICT). IEEE; 2017. p. 482\u201386.","DOI":"10.1109\/RTEICT.2017.8256643"},{"issue":"4","key":"2916_CR40","doi-asserted-by":"publisher","first-page":"235","DOI":"10.2478\/jaiscr-2019-0006","volume":"9","author":"A Shewalkar","year":"2019","unstructured":"Shewalkar A, Nyavanandi D, Ludwig SA. Performance evaluation of deep neural networks applied to speech recognition: RNN, LSTM and GRU. J Artif Intell Soft Comput Res. 2019;9(4):235\u201345.","journal-title":"J Artif Intell Soft Comput Res"},{"issue":"1","key":"2916_CR41","doi-asserted-by":"publisher","first-page":"9","DOI":"10.3390\/asi4010009","volume":"4","author":"Z Hu","year":"2021","unstructured":"Hu Z, Zhao Y, Khushi M. A survey of forex and stock price prediction using deep learning. Appl Syst Innov. 2021;4(1):9.","journal-title":"Appl Syst Innov"},{"issue":"8","key":"2916_CR42","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9(8):1735\u201380.","journal-title":"Neural Comput"},{"key":"2916_CR43","doi-asserted-by":"crossref","unstructured":"Sarode S, Tolani H G, Kak P, Lifna CS. Stock price prediction using machine learning techniques. In: 2019 international conference on intelligent sustainable systems (ICISS). IEEE; 2019. p. 177\u201381.","DOI":"10.1109\/ISS1.2019.8907958"},{"key":"2916_CR44","unstructured":"XIAOQIANG. What is a support vector machine? easyai.tech. 2019. https:\/\/easyai.tech\/en\/ai-definition\/svm. Accessed 30 Oct 2022."},{"issue":"8","key":"2916_CR45","first-page":"1931","volume":"14","author":"V Gururaj","year":"2019","unstructured":"Gururaj V, Shriya VR, Ashwini K. Stock market prediction using linear regression and support vector machines. Int J Appl Eng Res. 2019;14(8):1931\u20134.","journal-title":"Int J Appl Eng Res"},{"key":"2916_CR46","unstructured":"Kostadinov S. Gated Recurrent Unit. Understanding GRU Networks. 2017. https:\/\/medium.com\/towards-data-science\/understanding-gru-networks-2ef37df6c9be. Accessed 30 Oct 2022."}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-024-02916-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-024-02916-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-024-02916-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T20:44:34Z","timestamp":1719607474000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-024-02916-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,28]]},"references-count":46,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["2916"],"URL":"https:\/\/doi.org\/10.1007\/s42979-024-02916-z","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,28]]},"assertion":[{"value":"29 September 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 April 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 June 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that there are no potential conflicts of interest with respect to the research.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"686"}}