{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T08:12:00Z","timestamp":1773043920634,"version":"3.50.1"},"reference-count":21,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["1829704"],"award-info":[{"award-number":["1829704"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Digitization is changing our world, creating innovative finance channels and emerging technology such as cryptocurrencies, which are applications of blockchain technology. However, cryptocurrency price volatility is one of this technology\u2019s main trade-offs. In this paper, we explore a time series analysis using deep learning to study the volatility and to understand this behavior. We apply a long short-term memory model to learn the patterns within cryptocurrency close prices and to predict future prices. The proposed model learns from the close values. The performance of this model is evaluated using the root-mean-squared error and by comparing it to an ARIMA model.<\/jats:p>","DOI":"10.3390\/a15070230","type":"journal-article","created":{"date-parts":[[2022,7,2]],"date-time":"2022-07-02T11:12:35Z","timestamp":1656760355000},"page":"230","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Time Series Analysis of Cryptocurrency Prices Using Long Short-Term Memory"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1102-1910","authenticated-orcid":false,"given":"Jacques Phillipe","family":"Fleischer","sequence":"first","affiliation":[{"name":"Kendall Campus, The Honors College at Miami Dade College, 11011 SW 104th St, Miami, FL 33176, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9558-179X","authenticated-orcid":false,"given":"Gregor","family":"von Laszewski","sequence":"additional","affiliation":[{"name":"Biocomplexity Institute, University of Virginia, 994 Research Park Blvd, Charlottesville, VA 22911, USA"}]},{"given":"Carlos","family":"Theran","sequence":"additional","affiliation":[{"name":"Computer & Information Systems Department, Florida A&M University, 1333 Wahnish Way 308 A Benjamin Banneker Technical Bldg, Tallahassee, FL 32307, USA"}]},{"given":"Yohn Jairo","family":"Parra Bautista","sequence":"additional","affiliation":[{"name":"Computer & Information Systems Department, Florida A&M University, 1333 Wahnish Way 308 A Benjamin Banneker Technical Bldg, Tallahassee, FL 32307, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,1]]},"reference":[{"key":"ref_1","unstructured":"Schock, L. (2022, June 08). Thinking About Buying the Latest New Cryptocurrency or Token? | Investor.gov, Available online: https:\/\/www.investor.gov\/additional-resources\/spotlight\/directors-take\/thinking-about-buying-latest-new-cryptocurrency-or."},{"key":"ref_2","unstructured":"Green, J.S. (2022, June 08). Understanding Cryptocurrency Market Fluctuations. Available online: https:\/\/www.telegraph.co.uk\/business\/business-reporter\/cryptocurrency-market-fluctuations."},{"key":"ref_3","unstructured":"Shroff, R. (2022, June 29). When Blockchain Meets Artificial Intelligence\u2013The Startup-Medium. Available online: https:\/\/medium.com\/swlh\/when-blockchain-meets-artificial-intelligence-e448968d0482."},{"key":"ref_4","first-page":"694","article-title":"Time Series Classification of Cryptocurrency Price Trend Based on a Recurrent LSTM Neural Network","volume":"15","author":"Kwon","year":"2019","journal-title":"J. Inf. Process. 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Lett."},{"key":"ref_12","unstructured":"Azari, A. (2019). Bitcoin Price Prediction: An ARIMA Approach. arXiv."},{"key":"ref_13","unstructured":"Aroussi, R. (2022, June 29). Reliably Download Historical Market Data from with Python. Available online: https:\/\/aroussi.com\/post\/python-yahoo-finance."},{"key":"ref_14","unstructured":"Finance, Y. (2022, June 08). EOS USD (EOS-USD) Price History & Historical Data\u2014Yahoo Finance. Available online: https:\/\/finance.yahoo.com\/quote\/EOS-USD\/history?p=EOS-USD."},{"key":"ref_15","unstructured":"Loukas, S. (2022, June 29). Time-Series Forecasting: Predicting Stock Prices Using an LSTM Model. Available online: https:\/\/towardsdatascience.com\/lstm-time-series-forecasting-predicting-stock-prices-using-an-lstm-model-6223e9644a2f."},{"key":"ref_16","unstructured":"Patrawala, V. (2022, June 29). How (NOT) To Predict Stock Prices With LSTMs\u2014Towards Data Science. 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Available online: https:\/\/github.com\/cloudmesh\/cloudmesh-common."},{"key":"ref_21","unstructured":"Fleischer, J., von Laszewski, G., Theran, C., and Bautista, Y.J.P. (2022). Time Series Analysis of Blockchain-Based Cryptocurrency Price Changes. arXiv."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/15\/7\/230\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:41:39Z","timestamp":1760139699000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/15\/7\/230"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,1]]},"references-count":21,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["a15070230"],"URL":"https:\/\/doi.org\/10.3390\/a15070230","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,1]]}}}