{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T10:38:51Z","timestamp":1778063931237,"version":"3.51.4"},"reference-count":29,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,10,31]],"date-time":"2022-10-31T00:00:00Z","timestamp":1667174400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Telekom Malaysia Research &amp; Development","award":["RDTC\/221045"],"award-info":[{"award-number":["RDTC\/221045"]}]},{"name":"Telekom Malaysia Research &amp; Development","award":["FRGS\/1\/2021\/ICT02\/MMU\/02\/4"],"award-info":[{"award-number":["FRGS\/1\/2021\/ICT02\/MMU\/02\/4"]}]},{"name":"Telekom Malaysia Research &amp; Development","award":["MMUI\/220021"],"award-info":[{"award-number":["MMUI\/220021"]}]},{"DOI":"10.13039\/501100003093","name":"Fundamental Research Grant Scheme of the Ministry of Higher Education","doi-asserted-by":"publisher","award":["RDTC\/221045"],"award-info":[{"award-number":["RDTC\/221045"]}],"id":[{"id":"10.13039\/501100003093","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003093","name":"Fundamental Research Grant Scheme of the Ministry of Higher Education","doi-asserted-by":"publisher","award":["FRGS\/1\/2021\/ICT02\/MMU\/02\/4"],"award-info":[{"award-number":["FRGS\/1\/2021\/ICT02\/MMU\/02\/4"]}],"id":[{"id":"10.13039\/501100003093","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003093","name":"Fundamental Research Grant Scheme of the Ministry of Higher Education","doi-asserted-by":"publisher","award":["MMUI\/220021"],"award-info":[{"award-number":["MMUI\/220021"]}],"id":[{"id":"10.13039\/501100003093","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012024","name":"Multimedia University Internal Research","doi-asserted-by":"publisher","award":["RDTC\/221045"],"award-info":[{"award-number":["RDTC\/221045"]}],"id":[{"id":"10.13039\/100012024","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012024","name":"Multimedia University Internal Research","doi-asserted-by":"publisher","award":["FRGS\/1\/2021\/ICT02\/MMU\/02\/4"],"award-info":[{"award-number":["FRGS\/1\/2021\/ICT02\/MMU\/02\/4"]}],"id":[{"id":"10.13039\/100012024","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012024","name":"Multimedia University Internal Research","doi-asserted-by":"publisher","award":["MMUI\/220021"],"award-info":[{"award-number":["MMUI\/220021"]}],"id":[{"id":"10.13039\/100012024","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>Virtual currencies have been declared as one of the financial assets that are widely recognized as exchange currencies. The cryptocurrency trades caught the attention of investors as cryptocurrencies can be considered as highly profitable investments. To optimize the profit of the cryptocurrency investments, accurate price prediction is essential. In view of the fact that the price prediction is a time series task, a hybrid deep learning model is proposed to predict the future price of the cryptocurrency. The hybrid model integrates a 1-dimensional convolutional neural network and stacked gated recurrent unit (1DCNN-GRU). Given the cryptocurrency price data over the time, the 1-dimensional convolutional neural network encodes the data into a high-level discriminative representation. Subsequently, the stacked gated recurrent unit captures the long-range dependencies of the representation. The proposed hybrid model was evaluated on three different cryptocurrency datasets, namely Bitcoin, Ethereum, and Ripple. Experimental results demonstrated that the proposed 1DCNN-GRU model outperformed the existing methods with the lowest RMSE values of 43.933 on the Bitcoin dataset, 3.511 on the Ethereum dataset, and 0.00128 on the Ripple dataset.<\/jats:p>","DOI":"10.3390\/data7110149","type":"journal-article","created":{"date-parts":[[2022,10,31]],"date-time":"2022-10-31T23:26:32Z","timestamp":1667258792000},"page":"149","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Cryptocurrency Price Prediction with Convolutional Neural Network and Stacked Gated Recurrent Unit"],"prefix":"10.3390","volume":"7","author":[{"given":"Chuen Yik","family":"Kang","sequence":"first","affiliation":[{"name":"Faculty of Information Science and Technology, Multimedia University, Melaka 75450, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3679-8977","authenticated-orcid":false,"given":"Chin Poo","family":"Lee","sequence":"additional","affiliation":[{"name":"Faculty of Information Science and Technology, Multimedia University, Melaka 75450, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1929-7978","authenticated-orcid":false,"given":"Kian Ming","family":"Lim","sequence":"additional","affiliation":[{"name":"Faculty of Information Science and Technology, Multimedia University, Melaka 75450, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,31]]},"reference":[{"key":"ref_1","unstructured":"Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. Decentralized Business Review, Available online: https:\/\/www.debr.io\/article\/21260-bitcoin-a-peer-to-peer-electronic-cash-system."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Lim, J.Y., Lim, K.M., and Lee, C.P. (2021, January 13\u201315). Stacked Bidirectional Long Short-Term Memory for Stock Market Analysis. Proceedings of the 2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), Kota Kinabalu, Malaysia.","DOI":"10.1109\/IICAIET51634.2021.9573812"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Chong, L.S., Lim, K.M., and Lee, C.P. (2020, January 26\u201327). Stock Market Prediction using Ensemble of Deep Neural Networks. Proceedings of the 2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), Kota Kinabalu, Malaysia.","DOI":"10.1109\/IICAIET49801.2020.9257864"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Islam, M.R., and Nguyen, N. (2020). Comparison of financial models for stock price prediction. J. Risk Financ. Manag., 13.","DOI":"10.3390\/jrfm13080181"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"358","DOI":"10.3390\/telecom3020019","article-title":"Stock Market Prediction Using Microblogging Sentiment Analysis and Machine Learning","volume":"3","author":"Koukaras","year":"2022","journal-title":"Telecom"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Park, J., and Seo, Y.S. (2022). A Deep Learning-Based Action Recommendation Model for Cryptocurrency Profit Maximization. Electronics, 11.","DOI":"10.3390\/electronics11091466"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"51","DOI":"10.3390\/data7050051","article-title":"A Hybrid Stock Price Prediction Model Based on PRE and Deep Neural Network","volume":"7","author":"Manujakshi","year":"2022","journal-title":"Data"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Shahbazi, Z., and Byun, Y.C. (2022). Knowledge Discovery on Cryptocurrency Exchange Rate Prediction Using Machine Learning Pipelines. Sensors, 22.","DOI":"10.3390\/s22051740"},{"key":"ref_9","first-page":"102583","article-title":"A deep learning-based cryptocurrency price prediction scheme for financial institutions","volume":"55","author":"Patel","year":"2020","journal-title":"J. Inf. Secur. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Pintelas, E., Livieris, I.E., Stavroyiannis, S., Kotsilieris, T., and Pintelas, P. (2020, January 5\u20137). Investigating the problem of cryptocurrency price prediction: A deep learning approach. Proceedings of the IFIP International Conference on Artificial Intelligence Applications and Innovations, Neos Marmaras, Greece.","DOI":"10.1007\/978-3-030-49186-4_9"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Gao, P., Zhang, R., and Yang, X. (2020). The application of stock index price prediction with neural network. Math. Comput. Appl., 25.","DOI":"10.3390\/mca25030053"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Carta, S., Medda, A., Pili, A., Reforgiato Recupero, D., and Saia, R. (2018). Forecasting e-commerce products prices by combining an autoregressive integrated moving average (ARIMA) model and google trends data. Future Internet, 11.","DOI":"10.3390\/fi11010005"},{"key":"ref_13","first-page":"1","article-title":"Cryptocurrency price prediction using tweet volumes and sentiment analysis","volume":"1","author":"Abraham","year":"2018","journal-title":"SMU Data Sci. Rev."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Dutta, A., Kumar, S., and Basu, M. (2020). A gated recurrent unit approach to bitcoin price prediction. J. Risk Financ. Manag., 13.","DOI":"10.3390\/jrfm13020023"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Sin, E., and Wang, L. (2017, January 29\u201331). Bitcoin price prediction using ensembles of neural networks. Proceedings of the 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), Guilin, China.","DOI":"10.1109\/FSKD.2017.8393351"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yenido\u011fan, I., \u00c7ayir, A., Kozan, O., Da\u011f, T., and Arslan, \u00c7. (2018, January 20\u201323). Bitcoin forecasting using ARIMA and PROPHET. Proceedings of the 2018 3rd International Conference on Computer Science and Engineering (UBMK), Sarajevo, Bosnia and Herzegovina.","DOI":"10.1109\/UBMK.2018.8566476"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"McNally, S., Roche, J., and Caton, S. (2018, January 21\u201323). Predicting the price of bitcoin using machine learning. Proceedings of the 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP), Cambridge, UK.","DOI":"10.1109\/PDP2018.2018.00060"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Phaladisailoed, T., and Numnonda, T. (2018, January 24\u201326). Machine learning models comparison for bitcoin price prediction. Proceedings of the 2018 10th International Conference on Information Technology and Electrical Engineering (ICITEE), Bali, Indonesia.","DOI":"10.1109\/ICITEED.2018.8534911"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"132","DOI":"10.4236\/jmf.2020.101009","article-title":"Bitcoin price prediction based on deep learning methods","volume":"10","author":"Jiang","year":"2019","journal-title":"J. Math. Financ."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Politis, A., Doka, K., and Koziris, N. (2021, January 3\u20136). Ether price prediction using advanced deep learning models. Proceedings of the 2021 IEEE International Conference on Blockchain and Cryptocurrency (ICBC), Sydney, Australia.","DOI":"10.1109\/ICBC51069.2021.9461061"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"138633","DOI":"10.1109\/ACCESS.2021.3117848","article-title":"Deep learning-based cryptocurrency price prediction scheme with inter-dependent relations","volume":"9","author":"Tanwar","year":"2021","journal-title":"IEEE Access"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Livieris, I.E., Kiriakidou, N., Stavroyiannis, S., and Pintelas, P. (2021). An advanced CNN-LSTM model for cryptocurrency forecasting. Electronics, 10.","DOI":"10.3390\/electronics10030287"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"115378","DOI":"10.1016\/j.eswa.2021.115378","article-title":"Forecasting cryptocurrency price using convolutional neural networks with weighted and attentive memory channels","volume":"183","author":"Zhang","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"82804","DOI":"10.1109\/ACCESS.2020.2990659","article-title":"Stochastic neural networks for cryptocurrency price prediction","volume":"8","author":"Jay","year":"2020","journal-title":"IEEE Access"},{"key":"ref_25","first-page":"1","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_26","doi-asserted-by":"crossref","unstructured":"Saadah, S., and Whafa, A.A. (2020, January 5\u20136). Monitoring Financial Stability Based on Prediction of Cryptocurrencies Price Using Intelligent Algorithm. Proceedings of the 2020 International Conference on Data Science and Its Applications (ICoDSA), Bandung, Indonesia.","DOI":"10.1109\/ICoDSA50139.2020.9212968"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Derbentsev, V., Datsenko, N., Babenko, V., Pushko, O., and Pursky, O. (2020, January 6\u20139). Forecasting Cryptocurrency Prices Using Ensembles-Based Machine Learning Approach. Proceedings of the 2020 IEEE International Conference on Problems of Infocommunications. Science and Technology (PIC S&T), Kharkiv, Ukraine.","DOI":"10.1109\/PICST51311.2020.9468090"},{"key":"ref_28","unstructured":"Zielak (2022, May 17). Bitcoin historical Data. Available online: https:\/\/www.kaggle.com\/mczielinski\/Bitcoin-historical-data."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.jfds.2021.03.001","article-title":"Short-term bitcoin market prediction via machine learning","volume":"7","author":"Jaquart","year":"2021","journal-title":"J. Financ. Data Sci."}],"container-title":["Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5729\/7\/11\/149\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:06:32Z","timestamp":1760144792000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5729\/7\/11\/149"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,31]]},"references-count":29,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["data7110149"],"URL":"https:\/\/doi.org\/10.3390\/data7110149","relation":{},"ISSN":["2306-5729"],"issn-type":[{"value":"2306-5729","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,31]]}}}