Integrating Deep Learning and Statistical Methods for Cryptocurrency Price Forecasting: The case of Ripple

Guevara Rincon, John Fredy (2025) Integrating Deep Learning and Statistical Methods for Cryptocurrency Price Forecasting: The case of Ripple. Masters thesis, Dublin, National College of Ireland.

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Abstract

The cryptocurrency market, and especially digital assets like XRP (Ripple), are becoming more popular and gaining greater acceptance in the market every day. One of its main characteristics is volatility and complex price dynamics, which pose significant challenges for predicting this market. This study presents and compares two distinct approaches to training neural networks: the autoregressive integrated moving average (ARIMA) statistical model and the long short-term memory (LSTM) deep learning architecture. By evaluating the strengths and limitations of each method, this study aims to provide a deeper understanding of how statistical and deep learning techniques can be applied to obtain findings that can guide model selection strategies and establish a methodology for future research with a solid methodological and practical foundation in financial markets.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Byrne, Brian UNSPECIFIED
Uncontrolled Keywords:	Ripple; Cryptocurrency; ARIMA; Long Short-Term Memory; Deep Learning
Subjects:	H Social Sciences > HG Finance > Money > Digital currency > Cryptocurrencies H Social Sciences > HG Finance > Fintech T Technology > T Technology (General) > Information Technology > Fintech Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in FinTech
Depositing User:	Ciara O'Brien
Date Deposited:	20 Aug 2026 11:07
Last Modified:	20 Aug 2026 11:37
URI:	https://norma.ncirl.ie/id/eprint/9573

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