Time Series Analysis of Cryptocurrency Prices Using Long Short-Term Memory
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’s main trade-offs. In this paper, we explore a time series an...
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Format: | Article |
Language: | English |
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MDPI AG
2022-07-01
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Series: | Algorithms |
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Online Access: | https://www.mdpi.com/1999-4893/15/7/230 |
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author | Jacques Phillipe Fleischer Gregor von Laszewski Carlos Theran Yohn Jairo Parra Bautista |
author_facet | Jacques Phillipe Fleischer Gregor von Laszewski Carlos Theran Yohn Jairo Parra Bautista |
author_sort | Jacques Phillipe Fleischer |
collection | DOAJ |
description | 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’s 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. |
first_indexed | 2024-03-09T12:21:51Z |
format | Article |
id | doaj.art-7186a28b947f4bbf9744eec7fe94f0d3 |
institution | Directory Open Access Journal |
issn | 1999-4893 |
language | English |
last_indexed | 2024-03-09T12:21:51Z |
publishDate | 2022-07-01 |
publisher | MDPI AG |
record_format | Article |
series | Algorithms |
spelling | doaj.art-7186a28b947f4bbf9744eec7fe94f0d32023-11-30T22:39:37ZengMDPI AGAlgorithms1999-48932022-07-0115723010.3390/a15070230Time Series Analysis of Cryptocurrency Prices Using Long Short-Term MemoryJacques Phillipe Fleischer0Gregor von Laszewski1Carlos Theran2Yohn Jairo Parra Bautista3Kendall Campus, The Honors College at Miami Dade College, 11011 SW 104th St, Miami, FL 33176, USABiocomplexity Institute, University of Virginia, 994 Research Park Blvd, Charlottesville, VA 22911, USAComputer & Information Systems Department, Florida A&M University, 1333 Wahnish Way 308 A Benjamin Banneker Technical Bldg, Tallahassee, FL 32307, USAComputer & Information Systems Department, Florida A&M University, 1333 Wahnish Way 308 A Benjamin Banneker Technical Bldg, Tallahassee, FL 32307, USADigitization 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’s 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.https://www.mdpi.com/1999-4893/15/7/230predictioncryptocurrencyLSTM |
spellingShingle | Jacques Phillipe Fleischer Gregor von Laszewski Carlos Theran Yohn Jairo Parra Bautista Time Series Analysis of Cryptocurrency Prices Using Long Short-Term Memory Algorithms prediction cryptocurrency LSTM |
title | Time Series Analysis of Cryptocurrency Prices Using Long Short-Term Memory |
title_full | Time Series Analysis of Cryptocurrency Prices Using Long Short-Term Memory |
title_fullStr | Time Series Analysis of Cryptocurrency Prices Using Long Short-Term Memory |
title_full_unstemmed | Time Series Analysis of Cryptocurrency Prices Using Long Short-Term Memory |
title_short | Time Series Analysis of Cryptocurrency Prices Using Long Short-Term Memory |
title_sort | time series analysis of cryptocurrency prices using long short term memory |
topic | prediction cryptocurrency LSTM |
url | https://www.mdpi.com/1999-4893/15/7/230 |
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