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https://repository.unad.edu.co/handle/10596/64685Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | Ruiz Ayala, Julian Andres | |
| dc.coverage.spatial | udr_-_Cali | |
| dc.creator | Trochez Hernandez, Esteban | |
| dc.date.accessioned | 2024-11-25T15:28:22Z | |
| dc.date.available | 2024-11-25T15:28:22Z | |
| dc.date.created | 2024-11-13 | |
| dc.identifier.uri | https://repository.unad.edu.co/handle/10596/64685 | |
| dc.description | ||
| dc.description.abstract | ||
| dc.format | ||
| dc.title | Predicciones de Criptomonedas Utilizando un Modelo de Predicción Basado en el Algoritmo de Bosque Aleatorio | |
| dc.type | Proyecto aplicado | |
| dc.subject.keywords | Criptomoneda | |
| dc.subject.keywords | algoritmo | |
| dc.subject.keywords | CDIO | |
| dc.subject.keywords | predicciones | |
| dc.subject.keywords | Random Forest | |
| dc.description.abstractenglish | Cryptocurrencies have led many investors to try to predict the price of cryptocurrencies in order to try and increase their profits. To encourage these investors, a prediction tool is provided to help them make investment decisions. In this project, prediction models based on the Random Forest algorithm will be created to make predictions of cryptocurrency values. This proposed technique helps to create predictive models of the closing price based on selected characteristics. When making predictions, the results are evaluated from the validation set. The methodology that structures this project is the CDIO methodology, which is a tool to address complex problems in four stages: conception, design, implementation and operation. This article aims to create prediction models based on the Random Forest algorithm to make accurate predictions about the short-term valuation of Bitcoin and Ethereum cryptocurrencies. The results of all prediction models are compared and then analyzed to determine which is the most effective prediction model. In the end, after applying the model selected for prediction, this model will be compared with the results of the CNN-LSTM model mentioned in the literature review to demonstrate that the proposed model is an improvement in terms of prediction accuracy. | |
| dc.subject.category | Aprendizaje Automático | |
| Appears in Collections: | Ingeniería de Sistemas | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Trabajo_Aplicado_Esteban_Trochez.pdf | 1.62 MB | Adobe PDF |  View/Open |
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