{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T14:42:59Z","timestamp":1775745779480,"version":"3.50.1"},"reference-count":26,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2024,12,26]],"date-time":"2024-12-26T00:00:00Z","timestamp":1735171200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>This paper presents an analysis of stock price forecasting in the financial market, with an emphasis on approaches based on time series models and deep learning techniques. Fundamental concepts of technical analysis are explored, such as exponential and simple averages, and various global indices are analyzed to be used as inputs for machine learning models, including Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and XGBoost. The results show that while each model possesses distinct characteristics, selecting the most efficient approach heavily depends on the specific data and forecasting objectives. The complexity of advanced models such as XGBoost and GRU is reflected in their overall performance, suggesting that they can be particularly effective at capturing patterns and making accurate predictions in more complex time series, such as stock prices.<\/jats:p>","DOI":"10.3390\/computation13010003","type":"journal-article","created":{"date-parts":[[2024,12,26]],"date-time":"2024-12-26T04:31:16Z","timestamp":1735187476000},"page":"3","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Stock Price Prediction in the Financial Market Using Machine Learning Models"],"prefix":"10.3390","volume":"13","author":[{"given":"Diogo M.","family":"Teixeira","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Institute of Engineering\u2014Polytechnic of Porto (ISEP\/IPP), 4249-015 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7410-8872","authenticated-orcid":false,"given":"Ramiro S.","family":"Barbosa","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Institute of Engineering\u2014Polytechnic of Porto (ISEP\/IPP), 4249-015 Porto, Portugal"},{"name":"GECAD\u2014Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, ISEP\/IPP, 4249-015 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,26]]},"reference":[{"key":"ref_1","unstructured":"Jesse, A. (2024, September 28). Algorithmic Trading: Leveraging AI and ML in Finance. RapidInnovation. Available online: https:\/\/www.rapidinnovation.io\/post\/algorithmic-trading-leveraging-ai-and-ml-in-finance."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Shah, D., Isah, H., and Zulkernine, F. (2019). Stock Market Analysis: A Review and Taxonomy of Prediction Techniques. Int. J. Financ. Stud., 7.","DOI":"10.3390\/ijfs7020026"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.62836\/iaet.v2i1.162","article-title":"Stock Market Analysis and Prediction Using LSTM: A Case Study on Technology Stocks","volume":"2","author":"Li","year":"2023","journal-title":"Innov. Appl. Eng. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Sonkavde, G., Dharrao, D.S., Bongale, A.M., Deokate, S.T., Doreswamy, D., and Bhat, S.K. (2023). Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis, and Discussion of Implications. Int. J. Financ. Stud., 11.","DOI":"10.3390\/ijfs11030094"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"163815","DOI":"10.1109\/ACCESS.2021.3134138","article-title":"Impact of Hyperparameter Tuning on Machine Learning Models in Stock Price Forecasting","volume":"9","author":"Hoque","year":"2021","journal-title":"IEEE Access"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"120346","DOI":"10.1016\/j.eswa.2023.120346","article-title":"Stock Price Prediction with Optimized Deep LSTM Network Using Artificial Rabbits Optimization Algorithm","volume":"227","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Nabipour, M., Nayyeri, P., Jabani, H., Mosavi, A., Salwana, E., and Shamshirband, S. (2020). Deep Learning for Stock Market Prediction. Entropy, 22.","DOI":"10.20944\/preprints202003.0256.v1"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"126","DOI":"10.14445\/22315381\/IJETT-V71I6P215","article-title":"Time Series Forecasting Based on Deep Learning CNN-LSTM-GRU Model on Stock Prices","volume":"71","author":"Naufal","year":"2023","journal-title":"Int. J. Eng. Trends Technol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhang, J., Ye, L., and Lai, Y. (2023). Stock Price Prediction Using CNN-BiLSTM-Attention Model. Mathematics, 11.","DOI":"10.3390\/math11091985"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Mehtab, S., and Sen, J. (2020, January 5\u20136). Stock Price Prediction Using CNN and LSTM-Based Deep Learning Models. Proceedings of the 2020 International Conference on Decision Aid Sciences and Application (DASA), Chiangrai, Thailand.","DOI":"10.1109\/DASA51403.2020.9317207"},{"key":"ref_11","unstructured":"(2024, September 28). Yahoo Finance. Available online: https:\/\/finance.yahoo.com\/."},{"key":"ref_12","unstructured":"(2024, September 28). Pandas. Available online: https:\/\/pandas.pydata.org\/."},{"key":"ref_13","unstructured":"(2024, October 05). Scikit-Learn. Available online: https:\/\/scikit-learn.org."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.eswa.2019.03.029","article-title":"CNNpred: CNN-based stock market prediction using a diverse set of variables","volume":"129","author":"Hoseinzade","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_15","unstructured":"(2024, September 28). Federal Reserve Economic Data (FRED). Available online: https:\/\/fred.stlouisfed.org\/."},{"key":"ref_16","unstructured":"Kavya, D. (2024, September 30). Optimizing Performance: SelectKBest for Efficient Feature Selection in Machine Learning. Medium. Available online: https:\/\/medium.com\/@Kavya2099\/optimizing-performance-selectkbest-for-efficient-feature-selection-in-machine-learning-3b635905ed48."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"79","DOI":"10.3354\/cr030079","article-title":"Willmott and Kenji Matsuura. Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance","volume":"30","author":"Cort","year":"2005","journal-title":"Clim. Res."},{"key":"ref_18","unstructured":"Ken, S. (2024, September 30). Mean Squared Error. Encyclopedia Britannica, 2024. Available online: https:\/\/www.britannica.com\/science\/mean-squared-error."},{"key":"ref_19","unstructured":"(2024, September 30). Deepchecks. Root Mean Squared Error (RMSE). Available online: https:\/\/www.deepchecks.com\/glossary\/root-mean-square-error\/."},{"key":"ref_20","unstructured":"Scott, N. (2024, September 30). Coefficient of Determination: How to Calculate It and Interpret the Result. Investopedia. Available online: https:\/\/www.investopedia.com\/terms\/c\/coefficient-of-determination.asp."},{"key":"ref_21","unstructured":"(2024, October 04). Keras. Available online: https:\/\/keras.io."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Hu, Z., Zhao, Y., and Khushi, M. (2021). A Survey of Forex and Stock Price Prediction Using Deep Learning. Appl. Syst. Innov., 4.","DOI":"10.3390\/asi4010009"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"115102","DOI":"10.1016\/j.eswa.2021.115102","article-title":"A machine learning approach for forecasting hierarchical time series","volume":"182","author":"Mancuso","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_24","unstructured":"(2024, October 05). XGBoost Documentation. Available online: https:\/\/xgboost.readthedocs.io\/en\/latest\/python\/."},{"key":"ref_25","unstructured":"Prashant, B. (2024, October 05). A Guide on XGBoost Hyperparameters Tuning. Kaggle. Available online: https:\/\/www.kaggle.com\/code\/prashant111\/a-guide-on-xgboost-hyperparameters-tuning\/."},{"key":"ref_26","unstructured":"GeeksforGeeks (2024, October 23). GeeksforGeeks: A Computer Science Portal for Geeks. Available online: https:\/\/www.geeksforgeeks.org\/."}],"container-title":["Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-3197\/13\/1\/3\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T17:00:28Z","timestamp":1760115628000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-3197\/13\/1\/3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,26]]},"references-count":26,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,1]]}},"alternative-id":["computation13010003"],"URL":"https:\/\/doi.org\/10.3390\/computation13010003","relation":{},"ISSN":["2079-3197"],"issn-type":[{"value":"2079-3197","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,26]]}}}