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dc.contributor.authorPiccarreta Acosta, Riccardo
dc.contributor.authorArana A.Z.
dc.date.accessioned2024-10-15T16:59:38Z
dc.date.available2024-10-15T16:59:38Z
dc.date.issued2023
dc.identifier.citationPiccarreta, R., & Arana A.Z.. (2023). Cardano Cryptocurrency Price from Twitter. A Prediction Algorithm from Machine Learning. SSRG International Journal of Electronics and Communication Engineering. https://doi.org/10.14445/23488549/IJECE-V10I12P104es_PE
dc.identifier.issn23488549
dc.identifier.urihttps://hdl.handle.net/20.500.12724/21343
dc.description.abstractCryptocurrencies 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. © 2023 Seventh Sense Research Group®.en_EN
dc.formathtml
dc.language.isoeng
dc.publisherSeventh Sense Research Group
dc.rightsPendiente*
dc.sourceRepositorio Institucional Ulima
dc.sourceUniversidad de Lima
dc.subjectPendiente
dc.titleCardano Cryptocurrency Price from Twitter. A Prediction Algorithm from Machine Learning
dc.typeinfo:eu-repo/semantics/article
dc.type.otherArtículo en Scopus
dc.identifier.journalSSRG International Journal of Electronics and Communication Engineering
dc.subject.ocdePendiente
dc.identifier.doihttps://doi.org/10.14445/23488549/IJECE-V10I12P104
ulima.lineadeinvestigacionPendientees_PE
dc.contributor.studentPiccarreta Acosta, Riccardo (Ingeniería Industrial)
ulima.catPendiente
ulima.autor.afiliacionPendiente
ulima.autor.carreraPendiente
dc.identifier.isni121541816
dc.identifier.scopusid2-s2.0-85185473144


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