{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:45:44Z","timestamp":1742913944728,"version":"3.40.3"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031390586"},{"type":"electronic","value":"9783031390593"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-39059-3_6","type":"book-chapter","created":{"date-parts":[[2023,7,30]],"date-time":"2023-07-30T13:01:37Z","timestamp":1690722097000},"page":"84-100","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Automated Dual-Module Pipeline for Stock Prediction: Integrating N-Perception Period Power Strategy and NLP-Driven Sentiment Analysis for Enhanced Forecasting Accuracy and Investor Insight"],"prefix":"10.1007","author":[{"given":"Siddhant","family":"Singh","sequence":"first","affiliation":[]},{"given":"Archit","family":"Thanikella","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,31]]},"reference":[{"key":"6_CR1","doi-asserted-by":"crossref","unstructured":"Vargas, M.R., Dos Anjos, C.E.M., Bichara, G.L.G., Evsukoff, A.G.: Deep leaming for stock market prediction using technical indicators and financial news articles. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. IEEE (2018)","DOI":"10.1109\/IJCNN.2018.8489208"},{"key":"6_CR2","doi-asserted-by":"publisher","first-page":"114800","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. 177, 114800 (2021)","journal-title":"Expert Syst. Appl."},{"issue":"2","key":"6_CR3","first-page":"418","volume":"25","author":"WF Sharpe","year":"1970","unstructured":"Sharpe, W.F.: Efficient capital markets: a review of theory and empirical work: discussion. J. Financ. 25(2), 418\u2013420 (1970)","journal-title":"J. Financ."},{"issue":"2","key":"6_CR4","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1016\/0304-4076(80)90092-5","volume":"14","author":"CWJ Granger","year":"1980","unstructured":"Granger, C.W.J.: Long memory relationships and the aggregation of dynamic models. J. Econom. 14(2), 227\u2013238 (1980)","journal-title":"J. Econom."},{"key":"6_CR5","unstructured":"Vaswani, A., et al.: Attention is all you need. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"6_CR6","doi-asserted-by":"publisher","first-page":"107371","DOI":"10.1016\/j.measurement.2019.107371","volume":"152","author":"F Xu","year":"2020","unstructured":"Xu, F., Yang, F., Fan, X., Huang, Z., Tsui, K.L.: Extracting degradation trends for roller bearings by using a moving-average stacked auto-encoder and a novel exponential function. Measurement 152, 107371 (2020)","journal-title":"Measurement"},{"key":"6_CR7","doi-asserted-by":"publisher","first-page":"1778","DOI":"10.1016\/j.apenergy.2016.01.045","volume":"185","author":"X Liu","year":"2017","unstructured":"Liu, X., An, H., Wang, L., Jia, X.: An integrated approach to optimize moving average rules in the EUA futures market based on particle swarm optimization and genetic algorithms. Appl. Energy 185, 1778\u20131787 (2017)","journal-title":"Appl. Energy"},{"key":"6_CR8","doi-asserted-by":"crossref","unstructured":"Rubi, M.A., Chowdhury, S., Rahman, A.A.A., Meero, A., Zayed, N.M., Islam, K.M.A.: Fitting multi-layer feed forward neural network and autoregressive integrated moving average for dhaka stock exchange price predicting. Emerg. Sci. J. 6(5), 1046\u20131061 (2022)","DOI":"10.28991\/ESJ-2022-06-05-09"},{"issue":"3","key":"6_CR9","doi-asserted-by":"publisher","first-page":"249","DOI":"10.5267\/j.dsl.2019.2.001","volume":"8","author":"T Alam","year":"2019","unstructured":"Alam, T.: Forecasting exports and imports through artificial neural network and autoregressive integrated moving average. Decis. Sci. Lett. 8(3), 249\u2013260 (2019)","journal-title":"Decis. Sci. Lett."},{"key":"6_CR10","doi-asserted-by":"crossref","unstructured":"Engle, R.F., Granger, C.W.J.: Co-integration and error correction: representation, estimation, and testing. Econom.: J. Econom. Soc. 251\u2013276 (1987)","DOI":"10.2307\/1913236"},{"key":"6_CR11","doi-asserted-by":"crossref","unstructured":"Cakra, Y.E., Trisedya, B.D.: Stock price prediction using linear regression based on sentiment analysis. In: 2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS), pp. 147\u2013154. IEEE (2015)","DOI":"10.1109\/ICACSIS.2015.7415179"},{"key":"6_CR12","series-title":"Lecture Notes on Data Engineering and Communications Technologies","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1007\/978-3-030-38040-3_2","volume-title":"Innovative Data Communication Technologies and Application","author":"H Vachhani","year":"2020","unstructured":"Vachhani, H., et al.: Machine learning based stock market analysis: a short survey. In: Raj, J.S., Bashar, A., Ramson, S.R.J. (eds.) ICIDCA 2019. LNDECT, vol. 46, pp. 12\u201326. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-38040-3_2"},{"key":"6_CR13","unstructured":"Xie, Y., Jiang, H.: Stock market forecasting based on text mining technology: a support vector machine method. arXiv preprint arXiv:1909.12789 (2019)"},{"key":"6_CR14","doi-asserted-by":"publisher","first-page":"1168","DOI":"10.1016\/j.procs.2020.03.049","volume":"170","author":"A Moghar","year":"2020","unstructured":"Moghar, A., Hamiche, M.: Stock market prediction using LSTM recurrent neural network. Procedia Comput. Sci. 170, 1168\u20131173 (2020)","journal-title":"Procedia Comput. Sci."},{"key":"6_CR15","doi-asserted-by":"crossref","unstructured":"Oncharoen, P., Vateekul, P.: Deep learning for stock market prediction using event embedding and technical indicators. In: 2018 5th International Conference on Advanced Informatics: Concept Theory and Applications (ICAICTA), pp. 19\u201324. IEEE (2018)","DOI":"10.1109\/ICAICTA.2018.8541310"},{"key":"6_CR16","doi-asserted-by":"publisher","first-page":"3187","DOI":"10.1016\/j.matpr.2020.11.399","volume":"49","author":"D Kumar","year":"2022","unstructured":"Kumar, D., Sarangi, P.K., Verma, R.: A systematic review of stock market prediction using machine learning and statistical techniques. Mater. Today Proc. 49, 3187\u20133191 (2022)","journal-title":"Mater. Today Proc."},{"issue":"3","key":"6_CR17","doi-asserted-by":"publisher","first-page":"1139","DOI":"10.1111\/j.1540-6261.2007.01232.x","volume":"62","author":"PC Tetlock","year":"2007","unstructured":"Tetlock, P.C.: Giving content to investor sentiment: the role of media in the stock market. J. Financ. 62(3), 1139\u20131168 (2007)","journal-title":"J. Financ."},{"key":"6_CR18","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.sbspro.2011.10.562","volume":"26","author":"X Zhang","year":"2011","unstructured":"Zhang, X., Fuehres, H., Gloor, P.A.: Predicting stock market indicators through twitter \u201cI hope it is not as bad as I fear\u201d. Procedia Soc. Behav. Sci. 26, 55\u201362 (2011)","journal-title":"Procedia Soc. Behav. Sci."},{"issue":"1","key":"6_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jocs.2010.12.007","volume":"2","author":"J Bollen","year":"2011","unstructured":"Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. J. Comput. Sci. 2(1), 1\u20138 (2011)","journal-title":"J. Comput. Sci."},{"key":"6_CR20","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532\u20131543 (2014)","DOI":"10.3115\/v1\/D14-1162"},{"key":"6_CR21","unstructured":"Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)"},{"key":"6_CR22","doi-asserted-by":"crossref","unstructured":"Aasi, B., Imtiaz, S.A., Qadeer, H.A., Singarajah, M., Kashef, R.: Stock price prediction using a multivariate multistep LSTM: a sentiment and public engagement analysis model. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1\u20138. IEEE (2021)","DOI":"10.1109\/IEMTRONICS52119.2021.9422526"},{"key":"6_CR23","doi-asserted-by":"crossref","unstructured":"Mohan, S., Mullapudi, S., Sammeta, S., Vijayvergia, P., Anastasiu, D.C.: Stock price prediction using news sentiment analysis. In: 2019 IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService), pp. 205\u2013208. IEEE (2019)","DOI":"10.1109\/BigDataService.2019.00035"},{"key":"6_CR24","doi-asserted-by":"crossref","unstructured":"Chiong, R., Fan, Z., Hu, Z., Adam, M.T.P., Lutz, B., Neumann, D.: A sentiment analysis-based machine learning approach for financial market prediction via news disclosures. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 278\u2013279 (2018)","DOI":"10.1145\/3205651.3205682"},{"key":"6_CR25","doi-asserted-by":"crossref","unstructured":"Del\u00e9glise, H., Interdonato, R., B\u00e9gu\u00e9, A., d\u2019H\u00f4tel, E.M., Teisseire, M., Roche, M.: Food security prediction from heterogeneous data combining machine and deep learning methods. Expert Syst. Appl. 190, 116189 (2022)","DOI":"10.1016\/j.eswa.2021.116189"},{"issue":"7","key":"6_CR26","doi-asserted-by":"publisher","first-page":"e0180944","DOI":"10.1371\/journal.pone.0180944","volume":"12","author":"W Bao","year":"2017","unstructured":"Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PLoS ONE 12(7), e0180944 (2017)","journal-title":"PLoS ONE"},{"key":"6_CR27","unstructured":"Ding, X., Zhang, Y., Liu, T., Duan, J.: Deep learning for event-driven stock prediction. In: Twenty-Fourth International Joint Conference on Artificial Intelligence (2015)"},{"key":"6_CR28","unstructured":"Hall, M.A.: Correlation-based feature selection of discrete and numeric class machine learning (2000)"},{"key":"6_CR29","doi-asserted-by":"crossref","unstructured":"Waqar, M., Dawood, H., Guo, P., Shahnawaz, M.B., Ghazanfar, M.A.: Prediction of stock market by principal component analysis. In: 2017 13th International Conference on Computational Intelligence and Security (CIS), pp. 599\u2013602. IEEE (2017)","DOI":"10.1109\/CIS.2017.00139"},{"key":"6_CR30","first-page":"444","volume":"320","author":"S Lahmiri","year":"2018","unstructured":"Lahmiri, S.: Minute-ahead stock price forecasting based on singular spectrum analysis and support vector regression. Appl. Math. Comput. 320, 444\u2013451 (2018)","journal-title":"Appl. Math. Comput."},{"key":"6_CR31","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1016\/S0925-2312(01)00702-0","volume":"50","author":"GP Zhang","year":"2003","unstructured":"Zhang, G.P.: Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50, 159\u2013175 (2003)","journal-title":"Neurocomputing"},{"key":"6_CR32","doi-asserted-by":"crossref","unstructured":"Agarwal, V., Madhusudan, L., Babu Namburi, H.: Method and apparatus for stock performance prediction using momentum strategy along with social feedback. In: 2nd International Conference on Intelligent Technologies (CONIT). IEEE (2022)","DOI":"10.1109\/CONIT55038.2022.9848364"}],"container-title":["Communications in Computer and Information Science","Deep Learning Theory and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-39059-3_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,30]],"date-time":"2023-07-30T13:03:47Z","timestamp":1690722227000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-39059-3_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031390586","9783031390593"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-39059-3_6","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"31 July 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DeLTA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Deep Learning Theory and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Rome","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"delta2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/delta.scitevents.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"PRIMORIS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"42","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"9","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"22","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"21% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}