{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T22:39:08Z","timestamp":1773787148604,"version":"3.50.1"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031682070","type":"print"},{"value":"9783031682087","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\/978-3-031-68208-7_20","type":"book-chapter","created":{"date-parts":[[2024,8,14]],"date-time":"2024-08-14T06:02:44Z","timestamp":1723615364000},"page":"237-250","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Transforming Stock Price Forecasting: Deep Learning Architectures and\u00a0Strategic Feature Engineering"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-0702-743X","authenticated-orcid":false,"given":"Nguyen Quoc","family":"Anh","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5336-0566","authenticated-orcid":false,"given":"Ha Xuan","family":"Son","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,8,15]]},"reference":[{"key":"20_CR1","doi-asserted-by":"crossref","unstructured":"Garefalakis, A.: Determinant factors of Hong Kong stock market. Asset Pricing & Valuation eJournal, Capital Markets (2011)","DOI":"10.2139\/ssrn.1762162"},{"key":"20_CR2","doi-asserted-by":"crossref","unstructured":"Ho, S.-Y., Odhiambo, N.M.: Analysing the macroeconomic drivers of stock market development in the Philippines. Cogent Economics & Finance, 6 (2018)","DOI":"10.1080\/23322039.2018.1451265"},{"issue":"06","key":"20_CR3","doi-asserted-by":"publisher","first-page":"214","DOI":"10.47772\/IJRISS.2021.5609","volume":"05","author":"TU Chowdhury","year":"2021","unstructured":"Chowdhury, T.U., Islam, M.S.: ARIMA time series analysis in forecasting daily stock price of Chittagong Stock Exchange (CSE). Int. J. Res. Innov. Soc. Sci. 05(06), 214\u2013233 (2021). https:\/\/doi.org\/10.47772\/IJRISS.2021.5609","journal-title":"Int. J. Res. Innov. Soc. Sci."},{"key":"20_CR4","doi-asserted-by":"crossref","unstructured":"Bakar, N.A., Rosbi, S.: Modeling volatility for high-frequency data of cryptocurrency bitcoin price using generalized autoregressive conditional heteroskedasticity (GARCH) model. Int. J. Adv. Eng. Res. Sci. (2022)","DOI":"10.22161\/ijaers.99.62"},{"key":"20_CR5","unstructured":"Rahman, H.A.A., Azizi, M.F.R., Saruand, M.F., Shafie, N.A.: Forecasting of Malaysia gold price with exponential smoothing. J. Sci. Technol. (2022)"},{"key":"20_CR6","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1198\/tech.2003.s767","volume":"45","author":"GO Johnston","year":"2003","unstructured":"Johnston, G.O.: Statistical models and methods for lifetime data. Technometrics 45, 264\u2013265 (2003)","journal-title":"Technometrics"},{"key":"20_CR7","doi-asserted-by":"publisher","first-page":"28299","DOI":"10.1109\/ACCESS.2019.2901842","volume":"7","author":"M Wen","year":"2019","unstructured":"Wen, M., Li, P., Zhang, L., Chen, Y.: Stock market trend prediction using high-order information of time series. IEEE Access 7, 28299\u201328308 (2019)","journal-title":"IEEE Access"},{"key":"20_CR8","doi-asserted-by":"crossref","unstructured":"Mudassir, M., Unal, D., Hammoudeh, M.: Time-series forecasting of bitcoin prices using high-dimensional features: a machine learning approach. Neural Comput. Appl., 1\u201315 (2020)","DOI":"10.1007\/s00521-020-05129-6"},{"key":"20_CR9","doi-asserted-by":"crossref","unstructured":"Liu, X., Ni, Y., Yang, B.: Stock price prediction of apple based on SVM and KNN. BCP Bus. Manage. (2022)","DOI":"10.54691\/bcpbm.v34i.3107"},{"key":"20_CR10","doi-asserted-by":"crossref","unstructured":"Ren, Z., Yin, J., Yu, Y., Ma, F., Li, R.: Stock price prediction based on optimized random forest model. In: 2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML), pp. 777\u2013783 (2022)","DOI":"10.1109\/CACML55074.2022.00134"},{"key":"20_CR11","doi-asserted-by":"crossref","unstructured":"Sen, J., Mehtab, S.: Accurate stock price forecasting using robust and optimized deep learning models. In: 2021 International Conference on Intelligent Technologies (CONIT), pp. 1\u20139 (2021)","DOI":"10.1109\/CONIT51480.2021.9498565"},{"key":"20_CR12","doi-asserted-by":"crossref","unstructured":"Khan, S., Rabbani, M.R., Bashar, A., Kamal, M.: Stock price forecasting using deep learning model. In: 2021 International Conference on Decision Aid Sciences and Application (DASA), pp. 215\u2013219 (2021)","DOI":"10.1109\/DASA53625.2021.9682319"},{"key":"20_CR13","doi-asserted-by":"crossref","unstructured":"Wang, G., Fan, Y.: Research on stock price forecasting model based on deep learning. In: 2021 4th International Conference on Information Systems and Computer Aided Education (2021)","DOI":"10.1145\/3482632.3487545"},{"key":"20_CR14","unstructured":"Truong, T.: Prediction of stock price direction using XGBoost algorithm. Banking Sci. J. Vietnam (2023)"},{"key":"20_CR15","doi-asserted-by":"crossref","unstructured":"Chan, K., Li, F., Lin, T.-C., Lin, J.-C.: What do stock price levels tell us about the firms? Capital Structure & Payout Policies eJournal, Corporate Finance (2017)","DOI":"10.1016\/j.jcorpfin.2017.06.013"},{"key":"20_CR16","doi-asserted-by":"publisher","first-page":"3139","DOI":"10.21105\/joss.03139","volume":"6","author":"D L\u00fcdecke","year":"2021","unstructured":"L\u00fcdecke, D., Ben-Shachar, M.S., Patil, I., Waggoner, P.D., Makowski, D.: performance: An R package for assessment, comparison and testing of statistical models. J. Open Source Softw. 6, 3139 (2021)","journal-title":"J. Open Source Softw."},{"key":"20_CR17","unstructured":"Peng, H., Yang, Z.: An empirical study on stock price forecasting based on Arima model. Front. Soc., Sci. Technol. (2022)"},{"key":"20_CR18","unstructured":"Xing, J., Li, Y.: An optimization framework for stock price prediction based on statistical information and recursive model average \u2013 taking Arima model as an example. Cloud and Service-Oriented Computing (2022)"},{"key":"20_CR19","doi-asserted-by":"crossref","unstructured":"Endri, E., Aipama, W., Razak, A.R., Sari, L., Septiano, R.: Stock price volatility during the COVID-19 pandemic: the GARCH model. Investment Manage. Financ. Innov. (2021)","DOI":"10.21511\/imfi.18(4).2021.02"},{"key":"20_CR20","unstructured":"Tang, H., Chiu, K.C., Xu, L.: Finite mixture of Arma-GARCH model for stock price prediction (2003)"},{"key":"20_CR21","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1007\/s40822-018-0108-2","volume":"9","author":"O Poyser","year":"2018","unstructured":"Poyser, O.: Exploring the dynamics of bitcoin\u2019s price: a Bayesian structural time series approach. Eurasian Econ. Rev. 9, 29\u201360 (2018)","journal-title":"Eurasian Econ. Rev."},{"key":"20_CR22","unstructured":"Cornel, I.: Arima vs. machine learning in terms of equity market forecasting. Ann. Oradea Econ. Sci. (2021)"},{"key":"20_CR23","doi-asserted-by":"crossref","unstructured":"Makridakis, S.: Statistical and machine learning forecasting methods: concerns and ways forward. PLoS ONE, 13 (2018)","DOI":"10.1371\/journal.pone.0194889"},{"key":"20_CR24","unstructured":"Wibowo, F.D., Dang, T.-T., Wang, C.-N.: Forecasting Indonesia stock price using time series analysis and machine learning in R. In: Indonesian Scholars Scientific Summit Taiwan Proceeding (2022)"},{"key":"20_CR25","doi-asserted-by":"crossref","unstructured":"Mailinda, I.: Stock price prediction during the pandemic period with the SVM, BPNN, and LSTM algorithm. In: 2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), pp. 189\u2013194 (2021)","DOI":"10.1109\/ISRITI54043.2021.9702865"},{"key":"20_CR26","doi-asserted-by":"crossref","unstructured":"Strader, T., Rozycki, J., Root, T., Huang, Y.-H.: Machine learning stock market prediction studies: review and research directions. J. Int. Technol. Inf. Manage. (2020)","DOI":"10.58729\/1941-6679.1435"},{"key":"20_CR27","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1504\/IJAHUC.2020.104715","volume":"33","author":"SS Roy","year":"2020","unstructured":"Roy, S.S., Chopra, R., Lee, K.C., Spampinato, C., Mohammadi-ivatloo, B.: Random forest, gradient boosted machines and deep neural network for stock price forecasting: a comparative analysis on South Korean companies. Int. J. Ad Hoc Ubiquitous Comput. 33, 62\u201371 (2020)","journal-title":"Int. J. Ad Hoc Ubiquitous Comput."},{"key":"20_CR28","doi-asserted-by":"crossref","unstructured":"Pencina, M., Agostino, B., Vasan, R.: Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Statist. Med., 27 (2008)","DOI":"10.1002\/sim.2929"},{"key":"20_CR29","doi-asserted-by":"crossref","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.E.: Deep learning (2015)","DOI":"10.1038\/nature14539"},{"key":"20_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten, C., Khoshgoftaar, T.: A survey on image data augmentation for deep learning. J. Big Data 6, 1\u201348 (2019)","journal-title":"J. Big Data"},{"key":"20_CR31","doi-asserted-by":"crossref","unstructured":"Lu, Z.: Time series analysis and forecasting of China\u2019s energy production during COVID-19: statistical models vs machine learning models (2021)","DOI":"10.21203\/rs.3.rs-1074872\/v2"},{"key":"20_CR32","doi-asserted-by":"crossref","unstructured":"Huang, Z., Lin, Y.: A hybrid model combined deep learning approaches in stock price prediction. In: 2022 IEEE 2nd International Conference on Electronic Technology, Communication and Information (ICETCI), pp. 835\u2013838 (2022)","DOI":"10.1109\/ICETCI55101.2022.9832210"},{"key":"20_CR33","doi-asserted-by":"crossref","unstructured":"Chen, Y., et al.: Stock price forecast based on CNN-BILSTM-ECA model. Sci. Program. 2021, 2446543:1\u20132446543:20 (2021)","DOI":"10.1155\/2021\/2446543"},{"key":"20_CR34","doi-asserted-by":"publisher","first-page":"31297","DOI":"10.1109\/ACCESS.2022.3160797","volume":"10","author":"Akpehyr and Kilic","year":"2022","unstructured":"Akpehyr and Kilic: How to handle data imbalance and feature selection problems in CNN-based stock price forecasting. IEEE Access 10, 31297\u201331305 (2022)","journal-title":"IEEE Access"},{"key":"20_CR35","doi-asserted-by":"crossref","unstructured":"Visser, L.: The importance of predictor variables and feature selection in day-ahead electricity price forecasting. In: 2020 International Conference on Smart Energy Systems and Technologies (SEST), pp. 1\u20136 (2020)","DOI":"10.1109\/SEST48500.2020.9203273"},{"key":"20_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.116941","volume":"200","author":"G Ji","year":"2022","unstructured":"Ji, G.: An adaptive feature selection schema using improved technical indicators for predicting stock price movements. Expert Syst. Appl. 200, 116941 (2022)","journal-title":"Expert Syst. Appl."},{"key":"20_CR37","doi-asserted-by":"crossref","unstructured":"Panopoulou, E., Souropanis, I.: The role of technical indicators in exchange rate forecasting. Int. Finance eJournal (2017)","DOI":"10.2139\/ssrn.3049864"},{"key":"20_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3510413","volume":"54","author":"CFG Dos Santos","year":"2022","unstructured":"Dos Santos, C.F.G., Papa, J.P.: Avoiding overfitting: a survey on regularization methods for convolutional neural networks. ACM Comput. Surv. (CSUR) 54, 1\u201325 (2022)","journal-title":"ACM Comput. Surv. (CSUR)"}],"container-title":["Lecture Notes in Computer Science","Modeling Decisions for Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-68208-7_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T18:09:06Z","timestamp":1732644546000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-68208-7_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031682070","9783031682087"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-68208-7_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"15 August 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MDAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Modeling Decisions for Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tokyo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 August 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 August 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mdai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}