{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T15:58:33Z","timestamp":1778083113972,"version":"3.51.4"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031530814","type":"print"},{"value":"9783031530821","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-53082-1_10","type":"book-chapter","created":{"date-parts":[[2024,1,30]],"date-time":"2024-01-30T18:02:52Z","timestamp":1706637772000},"page":"110-129","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Comparative Analysis of\u00a0CNN Pre-trained Model for\u00a0Stock Market Trend Prediction"],"prefix":"10.1007","author":[{"given":"Jitendra Kumar","family":"Chauhan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tanveer","family":"Ahmed","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amit","family":"Sinha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,1,31]]},"reference":[{"key":"10_CR1","first-page":"864","volume":"107","author":"A Abhyankar","year":"1997","unstructured":"Abhyankar, A., Copeland, L., Wong, W.: Non-linear dynamics in financial markets: evidence and implications. Econ. J. 107, 864\u2013880 (1997)","journal-title":"Econ. J."},{"key":"10_CR2","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.jimonfin.2017.10.002","volume":"81","author":"D Hartman","year":"2018","unstructured":"Hartman, D., Hlinka, J.: Nonlinear dependencies in international stock market returns: are they predictable? J. Int. Money Financ. 81, 116\u2013135 (2018)","journal-title":"J. Int. Money Financ."},{"key":"10_CR3","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Proceedings of ICLR (2014)"},{"key":"10_CR4","doi-asserted-by":"crossref","unstructured":"He, K., et al.: Deep residual learning for image recognition. In: Proceedings of CVPR (2015)","DOI":"10.1109\/CVPR.2016.90"},{"key":"10_CR5","doi-asserted-by":"crossref","unstructured":"Szegedy, C., et al.: Inception-v3: rethinking the inception architecture for computer vision. In: Proceedings of CVPR (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"10_CR6","unstructured":"Wang, Z., Oates, T.: Imaging time-series to improve classification and imputation. In: 2015 International Conference on Image Processing (ICIP), pp. 2796\u20132800 (2015). IEEE"},{"key":"10_CR7","unstructured":"Box, G.E.P., Jenkins, G.M.: Time Series Analysis: Forecasting and Control. Holden-Day, ??? (1970)"},{"key":"10_CR8","doi-asserted-by":"crossref","unstructured":"Nelson, D.B.: Conditional heteroskedasticity in asset returns: A new approach. Econometrica (1991)","DOI":"10.2307\/2938260"},{"key":"10_CR9","unstructured":"Taylor, S.J.: Modelling financial time series (1986)"},{"key":"10_CR10","doi-asserted-by":"crossref","unstructured":"Brock, W., Lakonishok, J., LeBaron, B.: Simple technical trading rules and the stochastic properties of stock returns. J. Finance 47(5), 1731\u20131764 (1992)","DOI":"10.1111\/j.1540-6261.1992.tb04681.x"},{"key":"10_CR11","unstructured":"Wang, Z., Oates, T.: Encoding time series as images for visual inspection and classification using tiled convolutional neural networks. In: AAAI Workshops (2015)"},{"key":"10_CR12","unstructured":"Guo, Q., Song, Y., Li, X.: An innovative method for daily traffic flow forecasting using VLNN and GRNN. IEEE Access (2016)"},{"key":"10_CR13","unstructured":"Xie, J., Xu, X., Wang, S.: A comparison of denoising methods for chaotic time series with application to short-term traffic flow forecasting. Expert Systems with Applications (2016)"},{"key":"10_CR14","unstructured":"Lahmiri, S., Boukadoum, M.: Stock market forecasting using empirical mode decomposition coupled with neural network models. Neural Computing and Applications (2019)"},{"key":"10_CR15","unstructured":"Sezer, O.B., Ozbayoglu, M.: Financial time series forecasting with deep learning: A systematic literature review: 2005\u20132019. Applied Soft Computing (2018)"},{"key":"10_CR16","unstructured":"Zhang, W., Zhou, X., Yang, H., Wang, J.: Financial market prediction with a hybrid approach. Information Sciences (2019)"},{"key":"10_CR17","doi-asserted-by":"crossref","unstructured":"Patel, J.S., Shah, S., Thakkar, P., Kotecha, K.: Predicting stock market index using fusion of machine learning techniques. Expert Systems with Applications (2015)","DOI":"10.1016\/j.eswa.2014.10.031"},{"key":"10_CR18","unstructured":"Li, L., Li, Q., Li, D.: The ARIMA+GARCH model application in the forecasting of stock index. Mathematical Problems in Engineering (2020)"},{"key":"10_CR19","doi-asserted-by":"crossref","unstructured":"Tsantekidis, A., Passalis, N., Tefas, A., Kanniainen, J., Gabbouj, M., Iosifidis, A.: Forecasting stock prices from the limit order book using convolutional neural networks. In: Business Informatics (CBI), 2017 IEEE 19th Conference on (2017)","DOI":"10.1109\/CBI.2017.23"},{"key":"10_CR20","doi-asserted-by":"crossref","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 (2017)","DOI":"10.1371\/journal.pone.0180944"},{"key":"10_CR21","unstructured":"Hoseinzade, E., Haratizadeh, S.: Deep learning in prediction of stock market indices: A case study of tehran stock exchange. Financial Innovation (2019)"},{"key":"10_CR22","doi-asserted-by":"crossref","unstructured":"Makridakis, S., Spiliotis, E., Assimakopoulos, V.: Statistical and machine learning forecasting methods: Concerns and ways forward. PloS one (2018)","DOI":"10.1371\/journal.pone.0194889"},{"key":"10_CR23","doi-asserted-by":"crossref","unstructured":"Gu, S., Kelly, B., Xiu, D.: Empirical asset pricing via machine learning. The Review of Financial Studies (2020)","DOI":"10.1093\/rfs\/hhaa009"}],"container-title":["Communications in Computer and Information Science","Recent Trends in Image Processing and Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-53082-1_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,30]],"date-time":"2024-01-30T18:08:25Z","timestamp":1706638105000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-53082-1_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031530814","9783031530821"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-53082-1_10","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"31 January 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declaration of Competing Interest"}},{"value":"RTIP2R","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Recent Trends in Image Processing and Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Derby","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","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":"7 December 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 December 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"rtip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/rtip2r-conference.org\/2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT, Microsoft","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"216","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":"62","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":"0","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":"29% - 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":"2.39","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":"2.79","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}