{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T10:39:37Z","timestamp":1778063977331,"version":"3.51.4"},"reference-count":47,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T00:00:00Z","timestamp":1744156800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The cryptocurrency market presents immense opportunities and significant risks due to its high volatility. Accurate price forecasting is crucial for informed investment decisions, enabling investors to optimize portfolio allocation, mitigate risk, and potentially maximize returns. Existing forecasting methods often struggle with the inherent non-stationarity and complexity of cryptocurrency price dynamics. This study addresses this challenge by developing a system for high-frequency forecasting of the closing prices of ten leading cryptocurrencies. We compare various machine learning models, including recurrent neural networks (RNNs), time series analysis (ARIMA), and conventional regression algorithms, using minute-step Bitcoin price data over a 30-day period to predict prices 60 min ahead. Our findings demonstrate that the GRU neural network exhibits superior predictive accuracy (MAPE = 0.09%, MSE = 5954.89, RMSE = 77.17, MAE = 60.20), outperforming other models considered. This improved forecasting accuracy contributes to the existing literature by providing empirical evidence for GRU\u2019s effectiveness in the volatile cryptocurrency market and offers practical insights for investment strategies. A web application integrating the best-performing model further facilitates real-time price prediction for multiple cryptocurrencies.<\/jats:p>","DOI":"10.3390\/info16040300","type":"journal-article","created":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T03:32:53Z","timestamp":1744169573000},"page":"300","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["High-Frequency Cryptocurrency Price Forecasting Using Machine Learning Models: A Comparative Study"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4950-7593","authenticated-orcid":false,"given":"F\u00e1tima","family":"Rodrigues","sequence":"first","affiliation":[{"name":"Institute of Engineering, Polytechnic of Porto, Rua Dr. Ant\u00f3nio Bernardino de Almeida, 4249-015 Porto, Portugal"},{"name":"INESC-TEC, Institute for Systems and Computer Engineering, Technology and Science, R. Dr. Roberto Frias, 4200-465 Porto, Portugal"}]},{"given":"Miguel","family":"Machado","sequence":"additional","affiliation":[{"name":"Institute of Engineering, Polytechnic of Porto, Rua Dr. Ant\u00f3nio Bernardino de Almeida, 4249-015 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Attanasio, G., Garza, P., Cagliero, L., and Baralis, E. (2019). Quantitative Cryptocurrency Trading: Exploring the Use of Machine Learning Techniques, Association for Computing Machinery, Inc.","DOI":"10.1145\/3336499.3338003"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wang, H., and Zhou, X. (2022, January 25\u201328). Less is More: Bitcoin Volatility Forecast Using Feature Selection and Deep Learning Models. Proceedings of the 2022 IEEE 20th International Conference on Industrial Informatics (INDIN), Perth, Australia.","DOI":"10.1109\/INDIN51773.2022.9976100"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1617","DOI":"10.1007\/s10614-022-10262-6","article-title":"Bitcoin Price Prediction: A Machine Learning Sample Dimension Approach","volume":"61","author":"Ranjan","year":"2023","journal-title":"Comput. Econ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1525","DOI":"10.1007\/s10614-022-10310-1","article-title":"Price Prediction of Cryptocurrency Using a Multi-Layer Gated Recurrent Unit Network with Multi Features","volume":"62","author":"Patra","year":"2022","journal-title":"Comput. Econ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1186\/s40854-020-00217-x","article-title":"Forecasting and trading cryptocurrencies with machine learning under changing market conditions","volume":"7","author":"Godinho","year":"2021","journal-title":"Financ. Innov."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1825","DOI":"10.1007\/s12065-021-00592-z","article-title":"Bitcoin closing price movement prediction with optimal functional link neural networks","volume":"15","author":"Nayak","year":"2022","journal-title":"Evol. Intell."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"14021","DOI":"10.1007\/s00521-021-06043-1","article-title":"Smoothing and stationarity enforcement framework for deep learning time-series forecasting","volume":"33","author":"Livieris","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1007\/s00521-021-05903-0","article-title":"The random neural network in price predictions","volume":"34","author":"Serrano","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Mahayana, D., Madyaratri, S.A., and \u2019Abbas, M.F. (2022, January 3\u20134). Predicting Price Movement of the BTCUSDT Pair Using LightGBM Classification Modeling for Cryptocurrency Trading. Proceedings of the 2022 12th International Conference on System Engineering and Technology (ICSET), Bandung, Indonesia.","DOI":"10.1109\/ICSET57543.2022.10010808"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1186\/s40854-022-00336-7","article-title":"Hybrid data decomposition-based deep learning for Bitcoin prediction and algorithm trading","volume":"8","author":"Li","year":"2022","journal-title":"Financ. Innov."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1007\/s10479-021-04205-x","article-title":"Forecasting mid-price movement of Bitcoin futures using machine learning","volume":"330","author":"Akyildirim","year":"2021","journal-title":"Ann. Oper. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1007\/s10479-021-04000-8","article-title":"A differential evolution-based regression framework for forecasting Bitcoin price","volume":"306","author":"Jana","year":"2021","journal-title":"Ann. Oper. Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1007\/s10287-022-00430-2","article-title":"Forecasting financial time series with Boltzmann entropy through neural networks","volume":"19","author":"Grilli","year":"2022","journal-title":"Comput. Manag. Sci."},{"key":"ref_14","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_15","first-page":"45","article-title":"A new typology design of performance metrics to measure errors in machine learning regression algorithms","volume":"14","author":"Botchkarev","year":"2019","journal-title":"Interdiscip. J. Inf. Knowl. Manag."},{"key":"ref_16","first-page":"6737","article-title":"Time-series forecasting of Bitcoin prices using high-dimensional features: A machine learning approach","volume":"32","author":"Mudassir","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_17","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_18","doi-asserted-by":"crossref","unstructured":"Cho, K., van Merrienboer, B., Bahdanau, D., and Bengio, Y. (2014). On the Properties of Neural Machine Translation: Encoder-Decoder Approaches. arXiv.","DOI":"10.3115\/v1\/W14-4012"},{"key":"ref_19","unstructured":"Box, G.E.P., and Jenkins, G.M. (1970). Time Series Analysis: Forecasting and Control, Holden-Day."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Olive, D.J. (2017). Multiple Linear Regression, Springer International Publishing.","DOI":"10.1007\/978-3-319-55252-1"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-Vector Networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A scalable tree boosting system. Proceedings of the KDD \u201916: The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_24","first-page":"3156","article-title":"LightGBM: A Highly Efficient Gradient Boosting Decision Tree","volume":"30","author":"Ke","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Swati, S., and Mohan, A. (2022, January 22\u201324). Cryptocurrency Value Prediction with Boosting Models. Proceedings of the 2022 International Conference on Intelligent Innovations in Engineering and Technology (ICIIET), Coimbatore, India.","DOI":"10.1109\/ICIIET55458.2022.9967540"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Dempere, J.M., El-Agure, Z.A., and Memic, D. (2022, January 25\u201326). Data Selection to Train Machine Learning Models and Forecast Bitcoin Prices: Depth vs. Width. Proceedings of the 2022 8th International Conference on Information Technology Trends (ITT), Dubai, United Arab Emirates.","DOI":"10.1109\/ITT56123.2022.9863966"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Maqsood, U., Khuhawar, F.Y., Talpur, S., Jaskani, F.H., and Memon, A.A. (2022, January 14\u201317). Twitter Mining based Forecasting of Cryptocurrency using Sentimental Analysis of Tweets. Proceedings of the 2022 Global Conference on Wireless and Optical Technologies (GCWOT), Malaga, Spain.","DOI":"10.1109\/GCWOT53057.2022.9772923"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1007\/s42979-022-01291-x","article-title":"Forecasting Bitcoin Price Using Interval Graph and ANN Model: A Novel Approach","volume":"3","author":"Murugesan","year":"2022","journal-title":"SN Comput. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Mittal, M., and Geetha, G. (2022, January 25\u201327). Predicting Bitcoin Price using Machine Learning. Proceedings of the 2022 International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, India.","DOI":"10.1109\/ICCCI54379.2022.9740772"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Dinshaw, C., Jain, R., and Hussain, S.A.I. (2022, January 8\u20139). Statistical Scrutiny of the Prediction Capability of Different Time Series Machine Learning Models in Forecasting Bitcoin Prices. Proceedings of the 2022 IEEE 4th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA), Goa, India.","DOI":"10.1109\/ICCCMLA56841.2022.9989057"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Lyu, H. (2022, January 25\u201327). Cryptocurrency Price forecasting: A Comparative Study of Machine Learning Model in Short-Term Trading. Proceedings of the 2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML), Hangzhou, China.","DOI":"10.1109\/CACML55074.2022.00054"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Kristensen, J., Madrigal-Cianci, J.P., Felekis, G., and Liatsikou, M. (2022, January 22\u201325). Cryptocurrency Price Prediction With Multi-task Multi-step Sequence-to-Sequence Modeling. Proceedings of the 2022 IEEE International Conference on Blockchain (Blockchain), Espoo, Finland.","DOI":"10.1109\/Blockchain55522.2022.00018"},{"key":"ref_33","unstructured":"Leon, L.G.N.D., Gomez, R.C., Tacal, M.L.G., Taylar, J.V., Nojor, V.V., and Villanueva, A.R. (2022, January 10\u201311). Bitcoin Price Forecasting using Time-series Architectures. Proceedings of the 2022 International Conference on ICT for Smart Society (ICISS), Bandung, Indonesia."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1007\/s12559-021-09841-w","article-title":"Deep Learning Forecasting in Cryptocurrency High-Frequency Trading","volume":"13","author":"Lahmiri","year":"2021","journal-title":"Cogn. Comput."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1179","DOI":"10.1007\/s10115-022-01796-0","article-title":"A new method of ensemble learning: Case of cryptocurrency price prediction","volume":"65","author":"Rather","year":"2023","journal-title":"Knowl. Inf. Syst."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Yu, D. (2022, January 21\u201323). Cryptocurrency Price Prediction Based on Long-Term and Short-Term Integrated Learning. Proceedings of the 2022 IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA), Shenyang, China.","DOI":"10.1109\/ICPECA53709.2022.9718963"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Mishal, M.H., Rakhi, N.J., Rashid, F., Hamid, K., Morol, M.K., Jubair, A.A., and Nandi, D. (2022, January 17\u201319). Prediction of Cryptocurrency Price using Machine Learning Techniques and Public Sentiment Analysis. Proceedings of the 2022 25th International Conference on Computer and Information Technology (ICCIT), Cox\u2019s Bazar, Bangladesh.","DOI":"10.1109\/ICCIT57492.2022.10055524"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Tanwar, A., and Kumar, V. (2022, January 21\u201323). Prediction of Cryptocurrency prices using Transformers and Long Short term Neural Networks. Proceedings of the 2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP), Hyderabad, India.","DOI":"10.1109\/ICICCSP53532.2022.9862436"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1007\/s10878-022-00949-9","article-title":"Bitcoin daily price prediction through understanding blockchain transaction pattern with machine learning methods","volume":"45","author":"Li","year":"2023","journal-title":"J. Comb. Optim."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1235","DOI":"10.1007\/s11265-020-01624-0","article-title":"Forecasting Financial Time Series Using Robust Deep Adaptive Input Normalization","volume":"93","author":"Passalis","year":"2021","journal-title":"J. Signal Process. Syst."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Malhotra, B., Chandwani, C., Agarwala, P., and Mann, S. (2022, January 13\u201314). Bitcoin Price Prediction Using Machine Learning and Deep Learning Algorithms. Proceedings of the 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Noida, India.","DOI":"10.1109\/ICRITO56286.2022.9964677"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Son, Y., Vohra, S., Vakkalagadda, R., Zhu, M., Hirde, A., Kumar, S., and Rajaram, A. (2022, January 19\u201321). Using Transformers and Deep Learning with Stance Detection to Forecast Cryptocurrency Price Movement. Proceedings of the 13th International Conference on Information and Communication Technology Convergence (ICTC), Jeju Island, Republic of Korea.","DOI":"10.1109\/ICTC55196.2022.9953018"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Sharma, K.P., Singh, S.K., Choudhary, A., and Goel, H. (2023, January 19\u201320). Price Prediction of Bitcoin using Social Media Activities and Past Trends. Proceedings of the 2023 13th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, India.","DOI":"10.1109\/Confluence56041.2023.10048799"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Tejaswi, D.K., Chauhan, H., Lakshmi, T.J., Swetha, R., and Sri, N.N. (2022, January 24\u201326). Investigation of Ethereum Price Trends using Machine learning and Deep Learning Algorithms. Proceedings of the 2022 2nd International Conference on Intelligent Technologies (CONIT), Hubli, India.","DOI":"10.1109\/CONIT55038.2022.9848000"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Aravindan, J., and Sankara, R.K.V. (2022, January 1\u20133). Parent Coin based Cryptocurrency Price Prediction using Regression Techniques. Proceedings of the 2022 IEEE Region 10 Symposium (TENSYMP), Mumbai, India.","DOI":"10.1109\/TENSYMP54529.2022.9864452"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Hafez, S.M., Nainay, M.E., Abougabal, M., and Kosba, A. (2022, January 18\u201321). Ethereum Price Prediction using Topological Data Analysis. Proceedings of the 2022 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT), Alamein New City, Egypt.","DOI":"10.1109\/GCAIoT57150.2022.10019049"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Malsa, N., Vyas, V., and Gautam, J. (2021). RMSE calculation of LSTM models for predicting prices of different cryptocurrencies. Int. J. Syst. Assur. Eng. Manag., 1\u20139.","DOI":"10.1007\/s13198-021-01431-1"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/4\/300\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:12:55Z","timestamp":1760029975000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/4\/300"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,9]]},"references-count":47,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["info16040300"],"URL":"https:\/\/doi.org\/10.3390\/info16040300","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,9]]}}}