{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T06:52:03Z","timestamp":1772607123253,"version":"3.50.1"},"reference-count":98,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T00:00:00Z","timestamp":1765238400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>This study investigates the application of advanced deep learning models to forecast fossil energy prices, a critical factor influencing global economic stability. Unlike previous research, this study conducts a comparative analysis of Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), Bidirectional Long Short-Term Memory (Bi-LSTM), Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Deep Neural Network (DNN) models. The evaluation metrics employed include Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results reveal that recurrent architectures, particularly GRU, LSTM, and Bi-LSTM, consistently outperform feedforward and convolutional models, demonstrating superior ability to capture temporal dependencies and nonlinear dynamics in energy markets. In contrast, the RNN and DNN show relatively weaker generalization capabilities. Additionally, visualizations of actual versus predicted prices for each model further emphasize superior forecasting accuracy of recurrent models. The results highlight the potential of deep learning in enhancing investment and policy decisions. Additionally, the results provide significant implications for policymakers and investors by emphasizing the value of accurate energy price forecasting in mitigating market volatility, improving portfolio management, and supporting evidence-based energy policies.<\/jats:p>","DOI":"10.3390\/a18120776","type":"journal-article","created":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T15:50:02Z","timestamp":1765295402000},"page":"776","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Forecasting Fossil Energy Price Dynamics with Deep Learning: Implications for Global Energy Security and Financial Stability"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0527-302X","authenticated-orcid":false,"given":"Bilal Ahmed","family":"Memon","sequence":"first","affiliation":[{"name":"School of Business and Economics, Westminster International University in Tashkent, Tashkent 100047, Uzbekistan"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"383","DOI":"10.2307\/2325486","article-title":"Efficient capital markets","volume":"25","author":"Fama","year":"1970","journal-title":"J. Financ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.eswa.2014.07.040","article-title":"Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques","volume":"42","author":"Patel","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"10389","DOI":"10.1016\/j.eswa.2011.02.068","article-title":"Using artificial neural network models in stock market index prediction","volume":"38","author":"Guresen","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1007\/s40745-021-00344-x","article-title":"A Comprehensive Comparative Study of Artificial Neural Network (ANN) and Support Vector Machines (SVM) on Stock Forecasting","volume":"10","author":"Kurani","year":"2023","journal-title":"Ann. Data Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"102627","DOI":"10.1016\/j.irfa.2023.102627","article-title":"Nonlinear asset pricing in Chinese stock market: A deep learning approach","volume":"87","author":"Pan","year":"2023","journal-title":"Int. Rev. Financ. Anal."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1007\/s40745-017-0112-5","article-title":"Internet of things, real-time decision making, and artificial intelligence","volume":"4","author":"Tien","year":"2017","journal-title":"Ann. Data Sci."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ahn, M.J., and Chen, Y.-C. (2020, January 15\u201319). Artificial intelligence in government: Potentials, challenges, and the future. Proceedings of the 21st Annual International Conference on Digital Government Research, Seoul, Republic of Korea.","DOI":"10.1145\/3396956.3398260"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Acemoglu, D., and Restrepo, P. (2018). Artificial intelligence, automation, and work. The Economics of Artificial Intelligence: An Agenda, University of Chicago Press.","DOI":"10.3386\/w24196"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1002\/jsc.2403","article-title":"Artificial intelligence techniques in finance and financial markets: A survey of the literature","volume":"30","author":"Milana","year":"2021","journal-title":"Strateg. Chang."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2133","DOI":"10.1007\/s11831-020-09448-8","article-title":"A comprehensive survey on portfolio optimization, stock price and trend prediction using particle swarm optimization","volume":"28","author":"Thakkar","year":"2021","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"100015","DOI":"10.1016\/j.dajour.2021.100015","article-title":"The applications of artificial neural networks, support vector machines, and long\u2013short term memory for stock market prediction","volume":"2","author":"Chhajer","year":"2022","journal-title":"Decis. Anal. J."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Sutiene, K., Schwendner, P., Sipos, C., Lorenzo, L., Mirchev, M., Lameski, P., Kabasinskas, A., Tidjani, C., Ozturkkal, B., and Cerneviciene, J. (2024). Enhancing portfolio management using artificial intelligence: Literature review. Front. Artif. Intell., 7.","DOI":"10.3389\/frai.2024.1371502"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.ins.2022.11.139","article-title":"K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data","volume":"622","author":"Ikotun","year":"2023","journal-title":"Inf. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"33","DOI":"10.54097\/74414c90","article-title":"Application of machine learning-based k-means clustering for financial fraud detection","volume":"10","author":"Huang","year":"2024","journal-title":"Acad. J. Sci. Technol."},{"key":"ref_15","first-page":"87","article-title":"Evolving least squares support vector machines for stock market trend mining","volume":"13","author":"Yu","year":"2008","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1016\/j.enpol.2013.12.049","article-title":"Predicting oil price movements: A dynamic Artificial Neural Network approach","volume":"68","author":"Godarzi","year":"2014","journal-title":"Energy Policy"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"670","DOI":"10.1016\/j.asoc.2015.06.040","article-title":"A Na\u00efve SVM-KNN based stock market trend reversal analysis for Indian benchmark indices","volume":"35","author":"Nayak","year":"2015","journal-title":"Appl. Soft Comput."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/j.asoc.2016.08.026","article-title":"Stock trend prediction based on a new status box method and AdaBoost probabilistic support vector machine","volume":"49","author":"Zhang","year":"2016","journal-title":"Appl. Soft Comput."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"122470","DOI":"10.1016\/j.techfore.2023.122470","article-title":"Artificial neural network (ANN)-based estimation of the influence of COVID-19 pandemic on dynamic and emerging financial markets","volume":"190","author":"Naveed","year":"2023","journal-title":"Technol. Forecast. Soc. Change"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Mienye, I.D., Swart, T.G., and Obaido, G. (2024). Recurrent Neural Networks: A Comprehensive Review of Architectures, Variants, and Applications. Information, 15.","DOI":"10.20944\/preprints202408.0748.v1"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1111\/issj.12542","article-title":"Financial Modelling System Using Deep Neural Networks (DNNs) for Financial Risk Assessments","volume":"75","author":"Naveed","year":"2024","journal-title":"Int. Soc. Sci. J."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.neucom.2018.09.082","article-title":"Time series forecasting of petroleum production using deep LSTM recurrent networks","volume":"323","author":"Sagheer","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_23","first-page":"1421","article-title":"Unlocking Online Insights: LSTM Exploration and Transfer Learning Prospects","volume":"11","author":"Tahir","year":"2024","journal-title":"Ann. Data Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"114844","DOI":"10.1016\/j.eswa.2021.114844","article-title":"Deep sequence to sequence Bi-LSTM neural networks for day-ahead peak load forecasting","volume":"175","author":"Mughees","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Bhatt, D., Patel, C., Talsania, H., Patel, J., Vaghela, R., Pandya, S., Modi, K., and Ghayvat, H. (2021). CNN Variants for Computer Vision: History, Architecture, Application, Challenges and Future Scope. Electronics, 10.","DOI":"10.3390\/electronics10202470"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1007\/s10462-024-10721-6","article-title":"A review of convolutional neural networks in computer vision","volume":"57","author":"Zhao","year":"2024","journal-title":"Artif. Intell. Rev."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2066","DOI":"10.3390\/ai5040101","article-title":"Deep Learning in Finance: A Survey of Applications and Techniques","volume":"5","author":"Mienye","year":"2024","journal-title":"AI"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Abraham, R., Samad, M.E., Bakhach, A.M., El-Chaarani, H., Sardouk, A., Nemar, S.E., and Jaber, D. (2022). Forecasting a Stock Trend Using Genetic Algorithm and Random Forest. J. Risk Financ. Manag., 15.","DOI":"10.3390\/jrfm15050188"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1180","DOI":"10.1016\/j.iref.2022.04.003","article-title":"Forecasting Pakistan stock market volatility: Evidence from economic variables and the uncertainty index","volume":"80","author":"Ghani","year":"2022","journal-title":"Int. Rev. Econ. Financ."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"O\u2019Connor, C., Bahloul, M., Prestwich, S., and Visentin, A. (2025). A Review of Electricity Price Forecasting Models in the Day-Ahead, Intra-Day, and Balancing Markets. Energies, 18.","DOI":"10.3390\/en18123097"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Fang, B., and Zhang, P. (2016). Big data in finance. Big Data Concepts, Theories, and Applications, Springer.","DOI":"10.1007\/978-3-319-27763-9_11"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"9969357","DOI":"10.1155\/2021\/9969357","article-title":"A Deep Learning-Based Inventory Management and Demand Prediction Optimization Method for Anomaly Detection","volume":"2021","author":"Deng","year":"2021","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/S0960-0779(98)00295-1","article-title":"Fractal market hypothesis and two power-laws","volume":"11","author":"Weron","year":"2000","journal-title":"Chaos Solitons Fractals"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"102715","DOI":"10.1016\/j.resourpol.2022.102715","article-title":"Examining the efficiency and herding behavior of commodity markets using multifractal detrended fluctuation analysis. Empirical evidence from energy, agriculture, and metal markets","volume":"77","author":"Memon","year":"2022","journal-title":"Resour. Policy"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"e22694","DOI":"10.1016\/j.heliyon.2023.e22694","article-title":"Are clean energy markets efficient? A multifractal scaling and herding behavior analysis of clean and renewable energy markets before and during the COVID19 pandemic","volume":"9","author":"Memon","year":"2023","journal-title":"Heliyon"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"491","DOI":"10.2478\/amns.2021.1.00053","article-title":"Application of mathematical probabilistic statistical model of base\u2013FFCA financial data processing","volume":"7","author":"Li","year":"2022","journal-title":"Appl. Math. Nonlinear Sci."},{"key":"ref_37","unstructured":"Ratner, B. (2017). Statistical and Machine-Learning Data Mining:: Techniques for Better Predictive Modeling and Analysis of Big Data, Chapman and Hall\/CRC."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"116659","DOI":"10.1016\/j.eswa.2022.116659","article-title":"Machine learning techniques and data for stock market forecasting: A literature review","volume":"197","author":"Kumbure","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"13521","DOI":"10.1007\/s10462-023-10466-8","article-title":"Deep learning modelling techniques: Current progress, applications, advantages, and challenges","volume":"56","author":"Ahmed","year":"2023","journal-title":"Artif. Intell. Rev."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2162","DOI":"10.1016\/j.eswa.2014.10.031","article-title":"Predicting stock market index using fusion of machine learning techniques","volume":"42","author":"Patel","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.jfineco.2021.08.017","article-title":"Machine learning in the Chinese stock market","volume":"145","author":"Leippold","year":"2022","journal-title":"J. Financ. Econ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1186\/s40854-019-0138-0","article-title":"Predicting the daily return direction of the stock market using hybrid machine learning algorithms","volume":"5","author":"Zhong","year":"2019","journal-title":"Financ. Innov."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"107119","DOI":"10.1016\/j.knosys.2021.107119","article-title":"Technical analysis strategy optimization using a machine learning approach in stock market indices","volume":"225","author":"Ayala","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1431","DOI":"10.1017\/S0022109022001120","article-title":"Machine Learning and the Stock Market","volume":"58","author":"Brogaard","year":"2023","journal-title":"J. Financ. Quant. Anal."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.patrec.2020.05.033","article-title":"Simplified long short-term memory model for robust and fast prediction","volume":"136","author":"Liu","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1889","DOI":"10.1109\/TCE.2023.3335155","article-title":"Enhancing Traffic Flow Prediction in Intelligent Cyber-Physical Systems: A Novel Bi-LSTM-Based Approach With Kalman Filter Integration","volume":"70","author":"Aljebreen","year":"2024","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_48","first-page":"164","article-title":"Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms","volume":"26","author":"Nikou","year":"2019","journal-title":"Intell. Syst. Account. Financ. Manag."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"104426","DOI":"10.1016\/j.catena.2019.104426","article-title":"Comparing the prediction performance of a Deep Learning Neural Network model with conventional machine learning models in landslide susceptibility assessment","volume":"188","author":"Bui","year":"2020","journal-title":"CATENA"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1007\/s10614-021-10198-3","article-title":"Forecasting the Dynamic Correlation of Stock Indices Based on Deep Learning Method","volume":"61","author":"Ni","year":"2023","journal-title":"Comput. Econ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1016\/j.ejor.2017.11.054","article-title":"Deep learning with long short-term memory networks for financial market predictions","volume":"270","author":"Fischer","year":"2018","journal-title":"Eur. J. Oper. Res."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1049\/cit2.12059","article-title":"Stock market prediction using deep learning algorithms","volume":"8","author":"Mukherjee","year":"2023","journal-title":"CAAI Trans. Intell. Technol."},{"key":"ref_53","first-page":"200111","article-title":"A comprehensive review on multiple hybrid deep learning approaches for stock prediction","volume":"16","author":"Shah","year":"2022","journal-title":"Intell. Syst. Appl."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Selvin, S., Vinayakumar, R., Gopalakrishnan, E.A., Menon, V.K., and Soman, K.P. (2017, January 13\u201316). Stock price prediction using LSTM, RNN and CNN-sliding window model. Proceedings of the 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, India.","DOI":"10.1109\/ICACCI.2017.8126078"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"123740","DOI":"10.1016\/j.eswa.2024.123740","article-title":"Explainable deep learning model for stock price forecasting using textual analysis","volume":"249","author":"Abdullah","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"5861","DOI":"10.1007\/s00521-023-09369-0","article-title":"Stock price prediction: Comparison of different moving average techniques using deep learning model","volume":"36","author":"Billah","year":"2024","journal-title":"Neural Comput. Appl."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"3267","DOI":"10.1007\/s00500-023-09606-7","article-title":"Stock market forecasting using deep learning with long short-term memory and gated recurrent unit","volume":"28","author":"Sivadasan","year":"2024","journal-title":"Soft Comput."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Dip Das, J., Thulasiram, R.K., Henry, C., and Thavaneswaran, A. (2024). Encoder\u2013Decoder Based LSTM and GRU Architectures for Stocks and Cryptocurrency Prediction. J. Risk Financ. Manag., 17.","DOI":"10.20944\/preprints202403.1677.v1"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"3140","DOI":"10.1007\/s13132-024-02108-3","article-title":"Advanced Machine Learning for Financial Markets: A PCA-GRU-LSTM Approach","volume":"16","author":"Liu","year":"2024","journal-title":"J. Knowl. Econ."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"103915","DOI":"10.1016\/j.iref.2025.103915","article-title":"Hybrid ML models for volatility prediction in financial risk management","volume":"98","author":"Kumar","year":"2025","journal-title":"Int. Rev. Econ. Financ."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Rahman, M.S., and Reza, H. (2025). Hybrid Deep Learning Approaches for Accurate Electricity Price Forecasting: A Day-Ahead US Energy Market Analysis with Renewable Energy. Mach. Learn. Knowl. Extr., 7.","DOI":"10.3390\/make7040120"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Klyuev, R.V., Morgoev, I.D., Morgoeva, A.D., Gavrina, O.A., Martyushev, N.V., Efremenkov, E.A., and Mengxu, Q. (2022). Methods of Forecasting Electric Energy Consumption: A Literature Review. Energies, 15.","DOI":"10.3390\/en15238919"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1109\/OAJPE.2020.3029979","article-title":"Energy Forecasting: A Review and Outlook","volume":"7","author":"Hong","year":"2020","journal-title":"IEEE Open Access J. Power Energy"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Lahby, M., Al-Fuqaha, A., and Maleh, Y. (2022). Machine Learning Techniques for Renewable Energy Forecasting: A Comprehensive Review. Computational Intelligence Techniques for Green Smart Cities, Springer International Publishing.","DOI":"10.1007\/978-3-030-96429-0"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Benti, N.E., Chaka, M.D., and Semie, A.G. (2023). Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects. Sustainability, 15.","DOI":"10.20944\/preprints202303.0451.v1"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Nasreddin, D., Abdellaoui, Y., Cheracher, A., Aboutaleb, S., Benmoussa, Y., Sabbahi, I., El Makroum, R., Marrakchi, S.A., Khaldoun, A., and El Alami, A. (2023). Regression and Machine Learning Modeling Comparative Analysis of Morocco\u2019s Fossil Fuel Energy Forecast, Springer Nature.","DOI":"10.1007\/978-3-031-43520-1_21"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.energy.2019.04.077","article-title":"Long-term forecast of energy commodities price using machine learning","volume":"179","author":"Herrera","year":"2019","journal-title":"Energy"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"939","DOI":"10.3390\/en8020939","article-title":"Forecasting Fossil Fuel Energy Consumption for Power Generation Using QHSA-Based LSSVM Model","volume":"8","author":"Sun","year":"2015","journal-title":"Energies"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Li, S., Luo, L., and Li, J. Advanced Machine Learning Approaches for Predicting Energy and Fossil Fuel Consumption for Green Growth. Unconv. Resour., 100262. 2025, In Press, Journal Pre-proof, 100262.","DOI":"10.1016\/j.uncres.2025.100262"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A Fast Learning Algorithm for Deep Belief Nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"132306","DOI":"10.1016\/j.physd.2019.132306","article-title":"Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network","volume":"404","author":"Sherstinsky","year":"2020","journal-title":"Phys. D Nonlinear Phenom."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1207\/s15516709cog1402_1","article-title":"Finding Structure in Time","volume":"14","author":"Elman","year":"1990","journal-title":"Cogn. Sci."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1740","DOI":"10.1109\/TNNLS.2020.3043752","article-title":"Subtraction Gates: Another Way to Learn Long-Term Dependencies in Recurrent Neural Networks","volume":"33","author":"He","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"106181","DOI":"10.1016\/j.asoc.2020.106181","article-title":"Financial time series forecasting with deep learning: A systematic literature review: 2005\u20132019","volume":"90","author":"Sezer","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1016\/j.neunet.2005.06.042","article-title":"Framewise phoneme classification with bidirectional LSTM and other neural network architectures","volume":"18","author":"Graves","year":"2005","journal-title":"Neural Netw."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1080\/20964471.2019.1657720","article-title":"A survey of remote sensing image classification based on CNNs","volume":"3","author":"Song","year":"2019","journal-title":"Big Earth Data"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1007\/s13748-019-00203-0","article-title":"Convolutional neural network: A review of models, methodologies and applications to object detection","volume":"9","author":"Dhillon","year":"2020","journal-title":"Prog. Artif. Intell."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Naranjo-Torres, J., Mora, M., Hern\u00e1ndez-Garc\u00eda, R., Barrientos, R.J., Fredes, C., and Valenzuela, A. (2020). A Review of Convolutional Neural Network Applied to Fruit Image Processing. Appl. Sci., 10.","DOI":"10.3390\/app10103443"},{"key":"ref_80","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_81","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_82","doi-asserted-by":"crossref","unstructured":"Cho, K., Van Merri\u00ebnboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv.","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Cahuantzi, R., Chen, X., and G\u00fcttel, S. (2023). A Comparison of LSTM and GRU Networks for Learning Symbolic Sequences, Springer Nature.","DOI":"10.1007\/978-3-031-37963-5_53"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"235","DOI":"10.2478\/jaiscr-2019-0006","article-title":"Performance Evaluation of Deep Neural Networks Applied to Speech Recognition: RNN, LSTM and GRU","volume":"9","author":"Shewalkar","year":"2019","journal-title":"J. Artif. Intell. Soft Comput. Res."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"105524","DOI":"10.1016\/j.asoc.2019.105524","article-title":"Investigating the impact of data normalization on classification performance","volume":"97","author":"Singh","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Raju, V.N.G., Lakshmi, K.P., Jain, V.M., Kalidindi, A., and Padma, V. (2020, January 20\u201322). Study the Influence of Normalization\/Transformation process on the Accuracy of Supervised Classification. Proceedings of the 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, India.","DOI":"10.1109\/ICSSIT48917.2020.9214160"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"2604915","DOI":"10.1155\/2020\/2604915","article-title":"Multistep-Ahead Stock Price Forecasting Based on Secondary Decomposition Technique and Extreme Learning Machine Optimized by the Differential Evolution Algorithm","volume":"2020","author":"Tang","year":"2020","journal-title":"Math. Probl. Eng."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"e10820","DOI":"10.1016\/j.heliyon.2022.e10820","article-title":"The effect of global price movements on the energy sector commodity on bitcoin price movement during the COVID-19 pandemic","volume":"8","author":"Meiryani","year":"2022","journal-title":"Heliyon"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Sadorsky, P. (2021). A Random Forests Approach to Predicting Clean Energy Stock Prices. J. Risk Financ. Manag., 14.","DOI":"10.3390\/jrfm14020048"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Alshawarbeh, E., Abdulrahman, A.T., and Hussam, E. (2023). Statistical Modeling of High Frequency Datasets Using the ARIMA-ANN Hybrid. Mathematics, 11.","DOI":"10.3390\/math11224594"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"4644855","DOI":"10.1155\/2022\/4644855","article-title":"User Classification and Stock Market-Based Recommendation Engine Based on Machine Learning and Twitter Analysis","volume":"2022","author":"Achyutha","year":"2022","journal-title":"Math. Probl. Eng."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1016\/j.procs.2023.12.137","article-title":"Assessment of the impact of big data analysis on decision-making in stock trading processes","volume":"231","author":"Kalashnikov","year":"2024","journal-title":"Procedia Comput. Sci."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"1248","DOI":"10.1016\/j.physa.2019.04.106","article-title":"Network topology of FTSE 100 Index companies: From the perspective of Brexit","volume":"523","author":"Yao","year":"2019","journal-title":"Phys. A Stat. Mech. Its Appl."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"12777","DOI":"10.1007\/s11042-019-08453-9","article-title":"Dropout vs. batch normalization: An empirical study of their impact to deep learning","volume":"79","author":"Garbin","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"477","DOI":"10.3390\/ai2040030","article-title":"A Novel Cryptocurrency Price Prediction Model Using GRU, LSTM and bi-LSTM Machine Learning Algorithms","volume":"2","author":"Hamayel","year":"2021","journal-title":"AI"},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Alkhatib, K., Khazaleh, H., Alkhazaleh, H.A., Alsoud, A.R., and Abualigah, L. (2022). A New Stock Price Forecasting Method Using Active Deep Learning Approach. J. Open Innov. Technol. Mark. Complex., 8.","DOI":"10.3390\/joitmc8020096"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"e1519","DOI":"10.1002\/widm.1519","article-title":"Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020\u20132022","volume":"14","author":"Zhang","year":"2024","journal-title":"WIREs Data Min. Knowl. Discov."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1109\/MDAT.2019.2952336","article-title":"Are CNNs Reliable Enough for Critical Applications? An Exploratory Study","volume":"37","author":"Neggaz","year":"2020","journal-title":"IEEE Des. Test"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/12\/776\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T05:21:33Z","timestamp":1765430493000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/12\/776"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,9]]},"references-count":98,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["a18120776"],"URL":"https:\/\/doi.org\/10.3390\/a18120776","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,9]]}}}