Modelos de predicción del precio de Bitcoin mediante algoritmos de Machine Learning
Visualitza/Obre
Estadístiques de LA Referencia / Recolecta
Inclou dades d'ús des de 2022
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hdl:2117/375810
Correu electrònic de l'autoreruic1998gmail.com
Tipus de documentTreball Final de Grau
Data2022-10-21
Condicions d'accésAccés obert
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Abstract
Bitcoin was the origin of the cryptocurrency market in 2008, when a technical document was presented under the pseudonym "Satoshi Nakamoto", in which the concept, characteristics and vision of this cryptocurrency were set out. Bitcoin is currently the leading and most popular cryptocurrency in terms of market capitalisation. For this reason, since 2020, society has become very interested in understanding how it works and being able to benefit economically from investment. To do this, two methodologies are mainly used: technical or manual market analysis and Machine Learning algorithms. Technical analysis allows a human being to analyse a graph of a specific asset, based on the use of a set of tools that allow the prediction of the next movement and produce economic benefits. Even so, it is a methodology that involves a considerable amount of time as it requires a lot of training and time to acquire the necessary experience and obtain economic returns from it. The use of Machine Learning allows the creation of models that make predictions of the price of an asset in an automated way. This methodology complements and improves the predictions made by a person who uses technical or manual analysis to determine the next movement of any asset, as Machine Learning can process and treat amounts of information that a human being is not capable of retaining. Therefore, the main objective of this final degree work is to create models that allow predicting the price of Bitcoin by using automatic Machine Learning algorithms from a data file containing attributes related to the price of the cryptocurrency, a Data Set. With that purpose, the set of algorithms that have been chosen are Random Forest, Linear Regression and Time Series (Forecasting). The prediction models obtained by means of the previously mentioned algorithms have been evaluated in terms of regression errors and techniques such as Out of Bag, Cross Validation and Grid Search techniques have been applied in order to optimise the models. Finally, Bitcoin price prediction has been carried out with each of the models, among which the Linear Regression model stands out, as it obtained the best prediction results compared to the real price.
TitulacióGRAU EN ENGINYERIA TELEMÀTICA (Pla 2009)
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memoria.pdf | 2,108Mb | Visualitza/Obre |