Cardano cryptocurrency price from Twitter. A prediction algorithm from machine learning

Descripción del Articulo

Cryptocurrencies are a growing market that has attracted the attention of many investors in recent years. While cryptocurrencies offer a secure and decentralized form of payment, this market is highly volatile. Factors influencing price changes include the balance of supply and demand, its utility,...

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Detalles Bibliográficos
Autores: Piccarreta Acosta, Riccardo, Zavala Arana, Alejandra
Formato: tesis de grado
Fecha de Publicación:2024
Institución:Universidad de Lima
Repositorio:ULIMA-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.ulima.edu.pe:20.500.12724/21708
Enlace del recurso:https://hdl.handle.net/20.500.12724/21708
Nivel de acceso:acceso abierto
Materia:Algoritmos
Aprendizaje automático
Criptomonedas
Twitter
https://purl.org/pe-repo/ocde/ford#2.11.04
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dc.title.en_EN.fl_str_mv Cardano cryptocurrency price from Twitter. A prediction algorithm from machine learning
title Cardano cryptocurrency price from Twitter. A prediction algorithm from machine learning
spellingShingle Cardano cryptocurrency price from Twitter. A prediction algorithm from machine learning
Piccarreta Acosta, Riccardo
Algoritmos
Aprendizaje automático
Criptomonedas
Twitter
https://purl.org/pe-repo/ocde/ford#2.11.04
title_short Cardano cryptocurrency price from Twitter. A prediction algorithm from machine learning
title_full Cardano cryptocurrency price from Twitter. A prediction algorithm from machine learning
title_fullStr Cardano cryptocurrency price from Twitter. A prediction algorithm from machine learning
title_full_unstemmed Cardano cryptocurrency price from Twitter. A prediction algorithm from machine learning
title_sort Cardano cryptocurrency price from Twitter. A prediction algorithm from machine learning
author Piccarreta Acosta, Riccardo
author_facet Piccarreta Acosta, Riccardo
Zavala Arana, Alejandra
author_role author
author2 Zavala Arana, Alejandra
author2_role author
dc.contributor.advisor.fl_str_mv Quiroz Flores, Juan Carlos
dc.contributor.author.fl_str_mv Piccarreta Acosta, Riccardo
Zavala Arana, Alejandra
dc.subject.es_PE.fl_str_mv Algoritmos
Aprendizaje automático
Criptomonedas
Twitter
topic Algoritmos
Aprendizaje automático
Criptomonedas
Twitter
https://purl.org/pe-repo/ocde/ford#2.11.04
dc.subject.ocde.none.fl_str_mv https://purl.org/pe-repo/ocde/ford#2.11.04
description Cryptocurrencies are a growing market that has attracted the attention of many investors in recent years. While cryptocurrencies offer a secure and decentralized form of payment, this market is highly volatile. Factors influencing price changes include the balance of supply and demand, its utility, trading indicators, and market confidence. The present research aims to predict the price of the Cardano cryptocurrency by using machine learning techniques, specifically SVM, LSTM and BiLSTM models. In addition to accounting for financial indices, Twitter activity was used as a data source to measure market sentiment. The study analyzes various predictive horizons, including time ranges of 1 day, seven days, 14 days, 21 days and 30 days. The results obtained were validated with different performance indicators, and it was determined that the model predicts Cardano prices one month ahead with a MAPE of less than 22%, providing valuable information for investors interested in the volatile Cardano cryptocurrency market.
publishDate 2024
dc.date.accessioned.none.fl_str_mv 2024-12-12T14:41:52Z
dc.date.available.none.fl_str_mv 2024-12-12T14:41:52Z
dc.date.issued.fl_str_mv 2024
dc.type.none.fl_str_mv info:eu-repo/semantics/bachelorThesis
dc.type.other.none.fl_str_mv Tesis
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dc.identifier.citation.es_PE.fl_str_mv Piccarreta Acosta, R., & Zavala Arana, A. (2024). Cardano cryptocurrency price from Twitter. A prediction algorithm from machine learning [Tesis para optar el Título Profesional de Ingeniero Industrial, Universidad de Lima]. Repositorio Institucional de la Universidad de Lima. https://hdl.handle.net/20.500.12724/21708
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12724/21708
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identifier_str_mv Piccarreta Acosta, R., & Zavala Arana, A. (2024). Cardano cryptocurrency price from Twitter. A prediction algorithm from machine learning [Tesis para optar el Título Profesional de Ingeniero Industrial, Universidad de Lima]. Repositorio Institucional de la Universidad de Lima. https://hdl.handle.net/20.500.12724/21708
121541816
url https://hdl.handle.net/20.500.12724/21708
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartof.fl_str_mv SUNEDU
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dc.publisher.none.fl_str_mv Universidad de Lima
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publisher.none.fl_str_mv Universidad de Lima
dc.source.none.fl_str_mv Repositorio Institucional - Ulima
Universidad de Lima
reponame:ULIMA-Institucional
instname:Universidad de Lima
instacron:ULIMA
instname_str Universidad de Lima
instacron_str ULIMA
institution ULIMA
reponame_str ULIMA-Institucional
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spelling Quiroz Flores, Juan CarlosPiccarreta Acosta, RiccardoZavala Arana, Alejandra2024-12-12T14:41:52Z2024-12-12T14:41:52Z2024Piccarreta Acosta, R., & Zavala Arana, A. (2024). Cardano cryptocurrency price from Twitter. A prediction algorithm from machine learning [Tesis para optar el Título Profesional de Ingeniero Industrial, Universidad de Lima]. Repositorio Institucional de la Universidad de Lima. https://hdl.handle.net/20.500.12724/21708https://hdl.handle.net/20.500.12724/21708121541816Cryptocurrencies are a growing market that has attracted the attention of many investors in recent years. While cryptocurrencies offer a secure and decentralized form of payment, this market is highly volatile. Factors influencing price changes include the balance of supply and demand, its utility, trading indicators, and market confidence. The present research aims to predict the price of the Cardano cryptocurrency by using machine learning techniques, specifically SVM, LSTM and BiLSTM models. In addition to accounting for financial indices, Twitter activity was used as a data source to measure market sentiment. The study analyzes various predictive horizons, including time ranges of 1 day, seven days, 14 days, 21 days and 30 days. The results obtained were validated with different performance indicators, and it was determined that the model predicts Cardano prices one month ahead with a MAPE of less than 22%, providing valuable information for investors interested in the volatile Cardano cryptocurrency market.Cryptomonedas son un mercado creciente que ha atraído la atención de varios inversionistas en los últimos años. Si bien las criptomonedas ofrecen una forma de pago segura y descentralizada, este mercado es muy volátil. Los factores que influyen en los cambios de precios incluyen el equilibrio de la oferta y la demanda, su utilidad, los indicadores de trading y la confianza del mercado. El objetivo de la presente investigación es predecir el precio de la criptomoneda Cardano mediante el uso de técnicas de aprendizaje automático, específicamente modelos de SVM, LSTM y BiLSTM. Además de tomar en cuenta índices financieros, se utilizó como fuente de datos la actividad en Twitter para medir la opinión del mercado. El estudio analiza diversos horizontes predictivos, incluyendo rangos de tiempo de 1 día, 7 días, 14 días, 21 días y 30 días. Los resultados obtenidos fueron validados con distintos indicadores de desempeño y se determinó que el modelo predice precios de Cardano con un mes de anticipación con un MAPE menor al 22% brindando información valiosa para los inversores interesados en el mercado volátil de la criptomoneda Cardano.application/pdfengUniversidad de LimaPEinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/4.0/Repositorio Institucional - UlimaUniversidad de Limareponame:ULIMA-Institucionalinstname:Universidad de Limainstacron:ULIMAAlgoritmosAprendizaje automáticoCriptomonedasTwitterhttps://purl.org/pe-repo/ocde/ford#2.11.04Cardano cryptocurrency price from Twitter. A prediction algorithm from machine learninginfo:eu-repo/semantics/bachelorThesisTesisSUNEDUTítulo ProfesionalIngeniería IndustrialUniversidad de Lima. 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Cardano cryptocurrency price from Twitter. A prediction algorithm from machine learning
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