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Forecasting Bitcoin closing price series using linear regression and neural networks models

In this article we forecast daily closing price series of Bitcoin, Litecoin and Ethereum cryptocurrencies, using data on prices and volumes of prior days. Cryptocurrencies price behaviour is still largely unexplored, presenting new opportunities for researchers and economists to highlight similariti...

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Autores principales: Uras, Nicola, Marchesi, Lodovica, Marchesi, Michele, Tonelli, Roberto
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7924725/
https://www.ncbi.nlm.nih.gov/pubmed/33816930
http://dx.doi.org/10.7717/peerj-cs.279
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author Uras, Nicola
Marchesi, Lodovica
Marchesi, Michele
Tonelli, Roberto
author_facet Uras, Nicola
Marchesi, Lodovica
Marchesi, Michele
Tonelli, Roberto
author_sort Uras, Nicola
collection PubMed
description In this article we forecast daily closing price series of Bitcoin, Litecoin and Ethereum cryptocurrencies, using data on prices and volumes of prior days. Cryptocurrencies price behaviour is still largely unexplored, presenting new opportunities for researchers and economists to highlight similarities and differences with standard financial prices. We compared our results with various benchmarks: one recent work on Bitcoin prices forecasting that follows different approaches, a well-known paper that uses Intel, National Bank shares and Microsoft daily NASDAQ closing prices spanning a 3-year interval and another, more recent paper which gives quantitative results on stock market index predictions. We followed different approaches in parallel, implementing both statistical techniques and machine learning algorithms: the Simple Linear Regression (SLR) model for uni-variate series forecast using only closing prices, and the Multiple Linear Regression (MLR) model for multivariate series using both price and volume data. We used two artificial neural networks as well: Multilayer Perceptron (MLP) and Long short-term memory (LSTM). While the entire time series resulted to be indistinguishable from a random walk, the partitioning of datasets into shorter sequences, representing different price “regimes”, allows to obtain precise forecast as evaluated in terms of Mean Absolute Percentage Error(MAPE) and relative Root Mean Square Error (relativeRMSE). In this case the best results are obtained using more than one previous price, thus confirming the existence of time regimes different from random walks. Our models perform well also in terms of time complexity, and provide overall results better than those obtained in the benchmark studies, improving the state-of-the-art.
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spelling pubmed-79247252021-04-02 Forecasting Bitcoin closing price series using linear regression and neural networks models Uras, Nicola Marchesi, Lodovica Marchesi, Michele Tonelli, Roberto PeerJ Comput Sci Data Mining and Machine Learning In this article we forecast daily closing price series of Bitcoin, Litecoin and Ethereum cryptocurrencies, using data on prices and volumes of prior days. Cryptocurrencies price behaviour is still largely unexplored, presenting new opportunities for researchers and economists to highlight similarities and differences with standard financial prices. We compared our results with various benchmarks: one recent work on Bitcoin prices forecasting that follows different approaches, a well-known paper that uses Intel, National Bank shares and Microsoft daily NASDAQ closing prices spanning a 3-year interval and another, more recent paper which gives quantitative results on stock market index predictions. We followed different approaches in parallel, implementing both statistical techniques and machine learning algorithms: the Simple Linear Regression (SLR) model for uni-variate series forecast using only closing prices, and the Multiple Linear Regression (MLR) model for multivariate series using both price and volume data. We used two artificial neural networks as well: Multilayer Perceptron (MLP) and Long short-term memory (LSTM). While the entire time series resulted to be indistinguishable from a random walk, the partitioning of datasets into shorter sequences, representing different price “regimes”, allows to obtain precise forecast as evaluated in terms of Mean Absolute Percentage Error(MAPE) and relative Root Mean Square Error (relativeRMSE). In this case the best results are obtained using more than one previous price, thus confirming the existence of time regimes different from random walks. Our models perform well also in terms of time complexity, and provide overall results better than those obtained in the benchmark studies, improving the state-of-the-art. PeerJ Inc. 2020-07-06 /pmc/articles/PMC7924725/ /pubmed/33816930 http://dx.doi.org/10.7717/peerj-cs.279 Text en ©2020 Uras et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Data Mining and Machine Learning
Uras, Nicola
Marchesi, Lodovica
Marchesi, Michele
Tonelli, Roberto
Forecasting Bitcoin closing price series using linear regression and neural networks models
title Forecasting Bitcoin closing price series using linear regression and neural networks models
title_full Forecasting Bitcoin closing price series using linear regression and neural networks models
title_fullStr Forecasting Bitcoin closing price series using linear regression and neural networks models
title_full_unstemmed Forecasting Bitcoin closing price series using linear regression and neural networks models
title_short Forecasting Bitcoin closing price series using linear regression and neural networks models
title_sort forecasting bitcoin closing price series using linear regression and neural networks models
topic Data Mining and Machine Learning
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7924725/
https://www.ncbi.nlm.nih.gov/pubmed/33816930
http://dx.doi.org/10.7717/peerj-cs.279
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