Predicción del precio del bitcoin utilizando algoritmos de aprendizaje profundo

El mercado de criptomonedas está experimentando un rápido crecimiento, lo que lo convierte en una alternativa potencialmente más lucrativa que los mercados financieros convencionales. No obstante, esta expansión va de la mano con una significativa volatilidad, presentando así un desafío crucial. En...

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Fecha de publicación:: 2023

Institución:: Universidad del Rosario

Repositorio:: Repositorio EdocUR - U. Rosario

Idioma:: spa

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repository_id_str
dc.title.none.fl_str_mv	Predicción del precio del bitcoin utilizando algoritmos de aprendizaje profundo
dc.title.TranslatedTitle.none.fl_str_mv	Bitcoin price prediction using deep learning algorithms
title	Predicción del precio del bitcoin utilizando algoritmos de aprendizaje profundo
spellingShingle	Predicción del precio del bitcoin utilizando algoritmos de aprendizaje profundo Bitcoin Aprendizaje profundo LSTM GRU Criptomonedas Bitcoin Deep learning LSTM GRU Cryptocurrencies
title_short	Predicción del precio del bitcoin utilizando algoritmos de aprendizaje profundo
title_full	Predicción del precio del bitcoin utilizando algoritmos de aprendizaje profundo
title_fullStr	Predicción del precio del bitcoin utilizando algoritmos de aprendizaje profundo
title_full_unstemmed	Predicción del precio del bitcoin utilizando algoritmos de aprendizaje profundo
title_sort	Predicción del precio del bitcoin utilizando algoritmos de aprendizaje profundo
dc.contributor.advisor.none.fl_str_mv	Morales Pinto, Yiby Karolina
dc.subject.none.fl_str_mv	Bitcoin Aprendizaje profundo LSTM GRU Criptomonedas
topic	Bitcoin Aprendizaje profundo LSTM GRU Criptomonedas Bitcoin Deep learning LSTM GRU Cryptocurrencies
dc.subject.keyword.none.fl_str_mv	Bitcoin Deep learning LSTM GRU Cryptocurrencies
description	El mercado de criptomonedas está experimentando un rápido crecimiento, lo que lo convierte en una alternativa potencialmente más lucrativa que los mercados financieros convencionales. No obstante, esta expansión va de la mano con una significativa volatilidad, presentando así un desafío crucial. En el contexto de esta tesis de maestría, se desarrollaron modelos de predicción de series temporales para el precio de cierre de Bitcoin mediante el uso de algoritmos de aprendizaje profundo, tales como LSTM y GRU. Además, se llevó a cabo una comparación con modelos tradicionales como ARIMA, con el propósito de analizar y evaluar su rendimiento.
publishDate	2023
dc.date.accessioned.none.fl_str_mv	2023-11-24T15:34:29Z
dc.date.available.none.fl_str_mv	2023-11-24T15:34:29Z
dc.date.created.none.fl_str_mv	2023-10-23
dc.type.none.fl_str_mv	bachelorThesis
dc.type.coar.fl_str_mv	http://purl.org/coar/resource_type/c_7a1f
dc.type.document.none.fl_str_mv	Trabajo de grado
dc.type.spa.none.fl_str_mv	Trabajo de grado
dc.identifier.doi.none.fl_str_mv	https://doi.org/10.48713/10336_41755
dc.identifier.uri.none.fl_str_mv	https://repository.urosario.edu.co/handle/10336/41755
url	https://doi.org/10.48713/10336_41755 https://repository.urosario.edu.co/handle/10336/41755
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language	spa
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dc.rights.acceso.none.fl_str_mv	Abierto (Texto Completo)
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rights_invalid_str_mv	Attribution-ShareAlike 4.0 International Abierto (Texto Completo) http://creativecommons.org/licenses/by-sa/4.0/ http://purl.org/coar/access_right/c_abf2
dc.format.extent.none.fl_str_mv	29 pp
dc.format.mimetype.none.fl_str_mv	application/pdf
dc.publisher.spa.fl_str_mv	Universidad del Rosario
dc.publisher.department.spa.fl_str_mv	Escuela de Ingeniería, Ciencia y Tecnología
dc.publisher.program.spa.fl_str_mv	Maestría en Matemáticas Aplicadas y Ciencias de la Computación
institution	Universidad del Rosario
dc.source.bibliographicCitation.none.fl_str_mv	Aggarwal, Apoorva; Gupta, Isha; Garg, Novesh; Goel, Anurag (2019) Deep Learning Approach to Determine the Impact of Socio Economic Factors. En: 2019 Twelfth International Conference on Contemporary Computing (IC3). pp. 1-5 Sovbetov, Yhlas (2018) Factors Influencing Cryptocurrency Prices: Evidence from Bitcoin,. En: Journal of Economics and Financial Analysis. Vol. 2; No. 2; pp. 1-27 Lim, Jing Yee; Lim, Kian Ming; Lee, Chin Poo (2021) Stacked bidirectional long short-term memory for stock market analysis. En: 2021 IEEE International Conference on Artificial Intelligence in. pp. 1-5 Nakamoto, Satoshi (2008) Bitcoin: A Peer-to-Peer Electronic Cash System. Wang, Yaqi; Wang, Chunfeng; Sensoy, Ahmet; Yao, Shouyu; Cheng, Feiyang (2022) Can Investors’ Informed Trading Predict Cryptocurrency Returns? Evidence. En: Research in International Business and Finance. pp. 101683 : Elsevier; Freeda, S Ezhilin; Selvan, T C Ezhil; Hemanandhini, I G (2021) Prediction of Bitcoin Price using Deep Learning Model. En: 2021 5th International Conference on Electronics, Communication and. pp. 1702-1706 Disponible en: http://dx.doi.org/10.1109/ICECA52323.2021.9676048. Disponible en: 10.1109/ICECA52323.2021.9676048. Al Guindy, Mohamed (2021) Cryptocurrency price volatility and investor attention. En: International Review of Economics & Finance. Vol. 76; No. C; pp. 556-570 Al Guindy, Mohamed (2021) Cryptocurrency price volatility and investor attention. En: International Review of Economics & Finance. Vol. 76; pp. 556-570 : Elsevier; Kristoufek, Ladislav (2013) BitCoin meets Google Trends and Wikipedia: Quantifying the relationship. En: Scientific reports. Vol. 3; No. 1; pp. 1-7 : Nature Publishing Group; Sapuric, Svetlana; Kokkinaki, Angelika (2014) Bitcoin is volatile! Isn’t that right?. En: International conference on business information systems. pp. 255-265 Chong, Lu Sin; Lim, Kian Ming; Lee, Chin Poo (2020) Stock Market Prediction using Ensemble of Deep Neural Networks. En: 2020 IEEE 2nd International Conference on Artificial Intelligence in. pp. 1-5 Islam, Mohammad Rafiqul; Nguyen, Nguyet (2020) Comparison of financial models for stock price prediction. En: Journal of Risk and Financial Management. Vol. 13; No. 8; pp. 181 : MDPI; Koukaras, Paraskevas; Nousi, Christina; Tjortjis, Christos (2022) Stock Market Prediction Using Microblogging Sentiment Analysis and Machine. En: Telecom. Vol. 3; pp. 358-378 Park, Jaehyun; Seo, Yeong-Seok (2022) A Deep Learning-Based Action Recommendation Model for Cryptocurrency. En: Electronics. Vol. 11; No. 9; pp. 1466 : MDPI; Manujakshi, B C; Kabadi, Mohan Govindsa; Naik, Nagaraj (2022) A Hybrid Stock Price Prediction Model Based on PRE and Deep Neural Network. En: Data. Vol. 7; No. 5; pp. 51 : MDPI; Shahbazi, Zeinab; Byun, Yung-Cheol (2022) Knowledge Discovery on Cryptocurrency Exchange Rate Prediction Using. En: Sensors. Vol. 22; No. 5; pp. 1740 : MDPI; Patel, Mohil Maheshkumar; Tanwar, Sudeep; Gupta, Rajesh; Kumar, Neeraj (2020) A deep learning-based cryptocurrency price prediction scheme for financial. En: Journal of information security and applications. Vol. 55; pp. 102583 : Elsevier; Pintelas, Emmanuel; Livieris, Ioannis E; Stavroyiannis, Stavros; Kotsilieris, Theodore; Pintelas, Panagiotis (2020) Investigating the problem of cryptocurrency price prediction: a deep. En: IFIP International conference on artificial intelligence applications and. pp. 99-110 Gao, Penglei; Zhang, Rui; Yang, Xi (2020) The application of stock index price prediction with neural network. En: Mathematical and Computational Applications. Vol. 25; No. 3; pp. 53 : MDPI; Carta, Salvatore; Medda, Andrea; Pili, Alessio; Reforgiato Recupero, Diego; Saia, Roberto (2018) Forecasting e-commerce products prices by combining an autoregressive. En: Future Internet. Vol. 11; No. 1; pp. 5 : MDPI; Abraham, Jethin; Higdon, Daniel; Nelson, John; Ibarra, Juan (2018) Cryptocurrency price prediction using tweet volumes and sentiment analysis. En: SMU Data Science Review. Vol. 1; No. 3; pp. 1 Dutta, Aniruddha; Kumar, Saket; Basu, Meheli (2020) A gated recurrent unit approach to bitcoin price prediction. En: Journal of risk and financial management. Vol. 13; No. 2; pp. 23 : MDPI; Sin, Edwin; Wang, Lipo (2017) Bitcoin price prediction using ensembles of neural networks. En: 2017 13th International conference on natural computation, fuzzy systems. pp. 666-671 Madan, Isaac (2014) Automated Bitcoin Trading via Machine Learning Algorithms. Velankar, Siddhi; Valecha, Sakshi; Maji, Shreya (2018) Bitcoin price prediction using machine learning. En: 2018 20th International Conference on Advanced Communication Technology. pp. 144-147 Disponible en: http://dx.doi.org/10.23919/ICACT.2018.8323676. Disponible en: 10.23919/ICACT.2018.8323676. Yenidoğan, Işil; Çayir, Aykut; Kozan, Ozan; Dağ, Tuğçe; Arslan, Çiğdem (2018) Bitcoin forecasting using ARIMA and PROPHET. En: 2018 3rd International Conference on Computer Science and Engineering. pp. 621-624 McNally, Sean; Roche, Jason; Caton, Simon (2018) Predicting the price of bitcoin using machine learning. En: 2018 26th euromicro international conference on parallel, distributed and. pp. 339-343 Politis, Agis; Doka, Katerina; Koziris, Nectarios (2021) Ether price prediction using advanced deep learning models. En: 2021 IEEE International Conference on Blockchain and Cryptocurrency (ICBC). pp. 1-3 Fang, Fan; Chung, Waichung; Ventre, Carmine; Basios, Michail; Kanthan, Leslie; Li, Lingbo; Wu, Fan (2021) Ascertaining price formation in cryptocurrency markets with machine. En: The European Journal of Finance. pp. 1-23 : Taylor & Francis; Bengio, Yoshua (2014) Learning Phrase Representations using RNN Encoder–Decoder for Statistical. En: ACL Anthology. CoinMarketCap,; Cryptocurrency Prices, Charts And Market Capitalizations \| CoinMarketCap. Disponible en: https://coinmarketcap.com/. Community-driven Bitcoin Statistics and Services. Disponible en: https://coin.dance/stats. Khedr, Ahmed M; Arif, Ifra; P, Pravija Raj; El-Bannany, Magdi; Alhashmi, Saadat M; Sreedharan, Meenu (2021) Cryptocurrency price prediction using traditional statistical and. En: International Journal of Intelligent Systems in Accounting, Finance &. Vol. 28; No. 1; pp. 3-34 : Wiley; Disponible en: https://doi.org/10.1002/isaf.1488; http://dx.doi.org/10.1002/isaf.1488. Disponible en: 10.1002/isaf.1488. Anupriya,; Garg, Shruti (2018) Autoregressive Integrated Moving Average Model based Prediction of Bitcoin. Disponible en: https://doi.org/10.1109/icssit.2018.8748423; http://dx.doi.org/10.1109/icssit.2018.8748423. Disponible en: 10.1109/icssit.2018.8748423. Singh, Pawan K; Pandey, Alok K; Bose, Suvadeep (2022) A new grey system approach to forecast closing price of Bitcoin, Bionic,. En: Quality & Quantity. Vol. 57; No. 3; pp. 2429-2446 : Springer Science+Business Media; Disponible en: https://doi.org/10.1007/s11135-022-01463-0; http://dx.doi.org/10.1007/s11135-022-01463-0. Disponible en: 10.1007/s11135-022-01463-0. Pappas, Stylianos Sp; Ekonomou, Lambros; Karamousantas, D C; Chatzarakis, George E; Katsikas, Sokratis K; Liatsis, Panos (2008) Electricity demand loads modeling using AutoRegressive Moving Average. En: Energy. Vol. 33; No. 9; pp. 1353-1360 : Elsevier BV; Disponible en: https://doi.org/10.1016/j.energy.2008.05.008; http://dx.doi.org/10.1016/j.energy.2008.05.008. Disponible en: 10.1016/j.energy.2008.05.008. Zhang, H; Rudholm, N (2013) Modeling and Forecasting Regional Gdp in sweden Using Autoregressive. : Ning Cai; Disponible en: https://doi.org/10.1155/2021/1767708; http://dx.doi.org/10.1155/2021/1767708. Disponible en: 10.1155/2021/1767708. Anupriya,; Garg, Shruti (2018) Autoregressive Integrated Moving Average Model based Prediction of Bitcoin. Disponible en: https://doi.org/10.1109/icssit.2018.8748423; http://dx.doi.org/10.1109/icssit.2018.8748423. Disponible en: 10.1109/icssit.2018.8748423. Ismail, Zuhaimy; Yahya, Azmi; Shabri, Ani (2009) Forecasting Gold Prices Using Multiple Linear Regression Method. En: American Journal of Applied Sciences. Vol. 6; pp. 1509-1514 Xu, Xinghan; Ren, Weijie (2019) A Hybrid Model Based on a Two-Layer Decomposition Approach and an. En: Symmetry. Vol. 11; No. 5; pp. 610 : MDPI; Disponible en: https://doi.org/10.3390/sym11050610; http://dx.doi.org/10.3390/sym11050610. Disponible en: 10.3390/sym11050610. Wu, Chih-Hung; Lu, Chih-Chiang; Ma, Yu-Feng; Lu, Ruei-Shan (2018) A New Forecasting Framework for Bitcoin Price with LSTM. En: 2018 IEEE International Conference on Data Mining Workshops (ICDMW). pp. 168-175 Disponible en: http://dx.doi.org/10.1109/ICDMW.2018.00032. Disponible en: 10.1109/ICDMW.2018.00032. (2019) Long Short-Term Memory. En: Neural Computation. Vol. 11; No. 15; pp. 46 : Ronald Williams; Disponible en: https://doi.org/10.1162/neco.1997.9.8.1735; http://dx.doi.org/10.1162. Disponible en: 10.1162. Russell, Stuart; Norvig, Peter (2010) Artificial Intelligence: A Modern Approach. : Prentice Hall; Alvarez de Toledo, Pablo; Saavedra, Toledo; Crespo Marquez, Adolfo; Núñez, Fernando; Usabiaga, Carlos; Rebollo, Yolanda (2002) AUTOREGRESSIVE MODELS AND SYSTEM DYNAMICS. A CASE STUDY FOR THE LABOR.
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spelling	Morales Pinto, Yiby Karolina1020737718600Moreno Quintero, Emanuelle AlejandroMagíster en Matemáticas Aplicadas y Ciencias de la ComputaciónFull time7d936eec-1f2c-4770-bd02-b9b8672e6726-12023-11-24T15:34:29Z2023-11-24T15:34:29Z2023-10-23El mercado de criptomonedas está experimentando un rápido crecimiento, lo que lo convierte en una alternativa potencialmente más lucrativa que los mercados financieros convencionales. No obstante, esta expansión va de la mano con una significativa volatilidad, presentando así un desafío crucial. En el contexto de esta tesis de maestría, se desarrollaron modelos de predicción de series temporales para el precio de cierre de Bitcoin mediante el uso de algoritmos de aprendizaje profundo, tales como LSTM y GRU. Además, se llevó a cabo una comparación con modelos tradicionales como ARIMA, con el propósito de analizar y evaluar su rendimiento.The cryptocurrency market is experiencing rapid growth, making it a potentially more lucrative alternative to conventional financial markets. However, this expansion goes hand in hand with significant volatility, thus presenting a crucial challenge. In the context of this master's thesis, time series prediction models for the closing price of Bitcoin were developed using deep learning algorithms such as LSTM and GRU. In addition, a comparison was carried out with traditional models such as ARIMA, with the purpose of analyzing and evaluating their performance.29 ppapplication/pdfhttps://doi.org/10.48713/10336_41755 https://repository.urosario.edu.co/handle/10336/41755spaUniversidad del RosarioEscuela de Ingeniería, Ciencia y TecnologíaMaestría en Matemáticas Aplicadas y Ciencias de la ComputaciónAttribution-ShareAlike 4.0 InternationalAbierto (Texto Completo)http://creativecommons.org/licenses/by-sa/4.0/http://purl.org/coar/access_right/c_abf2Aggarwal, Apoorva; Gupta, Isha; Garg, Novesh; Goel, Anurag (2019) Deep Learning Approach to Determine the Impact of Socio Economic Factors. En: 2019 Twelfth International Conference on Contemporary Computing (IC3). pp. 1-5 Sovbetov, Yhlas (2018) Factors Influencing Cryptocurrency Prices: Evidence from Bitcoin,. En: Journal of Economics and Financial Analysis. Vol. 2; No. 2; pp. 1-27 Lim, Jing Yee; Lim, Kian Ming; Lee, Chin Poo (2021) Stacked bidirectional long short-term memory for stock market analysis. En: 2021 IEEE International Conference on Artificial Intelligence in. pp. 1-5 Nakamoto, Satoshi (2008) Bitcoin: A Peer-to-Peer Electronic Cash System. Wang, Yaqi; Wang, Chunfeng; Sensoy, Ahmet; Yao, Shouyu; Cheng, Feiyang (2022) Can Investors’ Informed Trading Predict Cryptocurrency Returns? Evidence. En: Research in International Business and Finance. pp. 101683 : Elsevier;Freeda, S Ezhilin; Selvan, T C Ezhil; Hemanandhini, I G (2021) Prediction of Bitcoin Price using Deep Learning Model. En: 2021 5th International Conference on Electronics, Communication and. pp. 1702-1706 Disponible en: http://dx.doi.org/10.1109/ICECA52323.2021.9676048. Disponible en: 10.1109/ICECA52323.2021.9676048.Al Guindy, Mohamed (2021) Cryptocurrency price volatility and investor attention. En: International Review of Economics & Finance. Vol. 76; No. C; pp. 556-570 Al Guindy, Mohamed (2021) Cryptocurrency price volatility and investor attention. En: International Review of Economics & Finance. Vol. 76; pp. 556-570 : Elsevier;Kristoufek, Ladislav (2013) BitCoin meets Google Trends and Wikipedia: Quantifying the relationship. En: Scientific reports. Vol. 3; No. 1; pp. 1-7 : Nature Publishing Group;Sapuric, Svetlana; Kokkinaki, Angelika (2014) Bitcoin is volatile! Isn’t that right?. En: International conference on business information systems. pp. 255-265 Chong, Lu Sin; Lim, Kian Ming; Lee, Chin Poo (2020) Stock Market Prediction using Ensemble of Deep Neural Networks. En: 2020 IEEE 2nd International Conference on Artificial Intelligence in. pp. 1-5 Islam, Mohammad Rafiqul; Nguyen, Nguyet (2020) Comparison of financial models for stock price prediction. En: Journal of Risk and Financial Management. Vol. 13; No. 8; pp. 181 : MDPI;Koukaras, Paraskevas; Nousi, Christina; Tjortjis, Christos (2022) Stock Market Prediction Using Microblogging Sentiment Analysis and Machine. En: Telecom. Vol. 3; pp. 358-378 Park, Jaehyun; Seo, Yeong-Seok (2022) A Deep Learning-Based Action Recommendation Model for Cryptocurrency. En: Electronics. Vol. 11; No. 9; pp. 1466 : MDPI;Manujakshi, B C; Kabadi, Mohan Govindsa; Naik, Nagaraj (2022) A Hybrid Stock Price Prediction Model Based on PRE and Deep Neural Network. En: Data. Vol. 7; No. 5; pp. 51 : MDPI;Shahbazi, Zeinab; Byun, Yung-Cheol (2022) Knowledge Discovery on Cryptocurrency Exchange Rate Prediction Using. En: Sensors. Vol. 22; No. 5; pp. 1740 : MDPI;Patel, Mohil Maheshkumar; Tanwar, Sudeep; Gupta, Rajesh; Kumar, Neeraj (2020) A deep learning-based cryptocurrency price prediction scheme for financial. En: Journal of information security and applications. Vol. 55; pp. 102583 : Elsevier;Pintelas, Emmanuel; Livieris, Ioannis E; Stavroyiannis, Stavros; Kotsilieris, Theodore; Pintelas, Panagiotis (2020) Investigating the problem of cryptocurrency price prediction: a deep. En: IFIP International conference on artificial intelligence applications and. pp. 99-110 Gao, Penglei; Zhang, Rui; Yang, Xi (2020) The application of stock index price prediction with neural network. En: Mathematical and Computational Applications. Vol. 25; No. 3; pp. 53 : MDPI;Carta, Salvatore; Medda, Andrea; Pili, Alessio; Reforgiato Recupero, Diego; Saia, Roberto (2018) Forecasting e-commerce products prices by combining an autoregressive. En: Future Internet. Vol. 11; No. 1; pp. 5 : MDPI;Abraham, Jethin; Higdon, Daniel; Nelson, John; Ibarra, Juan (2018) Cryptocurrency price prediction using tweet volumes and sentiment analysis. En: SMU Data Science Review. Vol. 1; No. 3; pp. 1 Dutta, Aniruddha; Kumar, Saket; Basu, Meheli (2020) A gated recurrent unit approach to bitcoin price prediction. En: Journal of risk and financial management. Vol. 13; No. 2; pp. 23 : MDPI;Sin, Edwin; Wang, Lipo (2017) Bitcoin price prediction using ensembles of neural networks. En: 2017 13th International conference on natural computation, fuzzy systems. pp. 666-671 Madan, Isaac (2014) Automated Bitcoin Trading via Machine Learning Algorithms. Velankar, Siddhi; Valecha, Sakshi; Maji, Shreya (2018) Bitcoin price prediction using machine learning. En: 2018 20th International Conference on Advanced Communication Technology. pp. 144-147 Disponible en: http://dx.doi.org/10.23919/ICACT.2018.8323676. Disponible en: 10.23919/ICACT.2018.8323676.Yenidoğan, Işil; Çayir, Aykut; Kozan, Ozan; Dağ, Tuğçe; Arslan, Çiğdem (2018) Bitcoin forecasting using ARIMA and PROPHET. En: 2018 3rd International Conference on Computer Science and Engineering. pp. 621-624 McNally, Sean; Roche, Jason; Caton, Simon (2018) Predicting the price of bitcoin using machine learning. En: 2018 26th euromicro international conference on parallel, distributed and. pp. 339-343 Politis, Agis; Doka, Katerina; Koziris, Nectarios (2021) Ether price prediction using advanced deep learning models. En: 2021 IEEE International Conference on Blockchain and Cryptocurrency (ICBC). pp. 1-3 Fang, Fan; Chung, Waichung; Ventre, Carmine; Basios, Michail; Kanthan, Leslie; Li, Lingbo; Wu, Fan (2021) Ascertaining price formation in cryptocurrency markets with machine. En: The European Journal of Finance. pp. 1-23 : Taylor & Francis;Bengio, Yoshua (2014) Learning Phrase Representations using RNN Encoder–Decoder for Statistical. En: ACL Anthology.CoinMarketCap,; Cryptocurrency Prices, Charts And Market Capitalizations \| CoinMarketCap. Disponible en: https://coinmarketcap.com/. Community-driven Bitcoin Statistics and Services. Disponible en: https://coin.dance/stats.Khedr, Ahmed M; Arif, Ifra; P, Pravija Raj; El-Bannany, Magdi; Alhashmi, Saadat M; Sreedharan, Meenu (2021) Cryptocurrency price prediction using traditional statistical and. En: International Journal of Intelligent Systems in Accounting, Finance &. Vol. 28; No. 1; pp. 3-34 : Wiley; Disponible en: https://doi.org/10.1002/isaf.1488; http://dx.doi.org/10.1002/isaf.1488. Disponible en: 10.1002/isaf.1488.Anupriya,; Garg, Shruti (2018) Autoregressive Integrated Moving Average Model based Prediction of Bitcoin. Disponible en: https://doi.org/10.1109/icssit.2018.8748423; http://dx.doi.org/10.1109/icssit.2018.8748423. Disponible en: 10.1109/icssit.2018.8748423.Singh, Pawan K; Pandey, Alok K; Bose, Suvadeep (2022) A new grey system approach to forecast closing price of Bitcoin, Bionic,. En: Quality & Quantity. Vol. 57; No. 3; pp. 2429-2446 : Springer Science+Business Media; Disponible en: https://doi.org/10.1007/s11135-022-01463-0; http://dx.doi.org/10.1007/s11135-022-01463-0. Disponible en: 10.1007/s11135-022-01463-0.Pappas, Stylianos Sp; Ekonomou, Lambros; Karamousantas, D C; Chatzarakis, George E; Katsikas, Sokratis K; Liatsis, Panos (2008) Electricity demand loads modeling using AutoRegressive Moving Average. En: Energy. Vol. 33; No. 9; pp. 1353-1360 : Elsevier BV; Disponible en: https://doi.org/10.1016/j.energy.2008.05.008; http://dx.doi.org/10.1016/j.energy.2008.05.008. Disponible en: 10.1016/j.energy.2008.05.008.Zhang, H; Rudholm, N (2013) Modeling and Forecasting Regional Gdp in sweden Using Autoregressive. : Ning Cai; Disponible en: https://doi.org/10.1155/2021/1767708; http://dx.doi.org/10.1155/2021/1767708. Disponible en: 10.1155/2021/1767708.Anupriya,; Garg, Shruti (2018) Autoregressive Integrated Moving Average Model based Prediction of Bitcoin. 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Predicción del precio del bitcoin utilizando algoritmos de aprendizaje profundo

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