Accurate, Secure and Explainable bitcoin forecasting
Year: 2025
Type: article
Abstract: Forecasting the price of bitcoin assets is a difficult task, especially as bitcoins are highly volatile and speculative. In this paper we leverage the non linear capability of deep and machine learning models to enhance bitcoin forecasts. We propose a systematic comparison of different deep learning and machine learning models, based on their Accuracy, Security and Explainability characteristics. The empirical findings reveal that, while CNN-GRU, GRU and LSTM are the most accurate models, for maximum cumulative return and risk adjusted performance GRU and CNN are preferred. Whereas, for transparent and stable decision-making, Random Forest and XGboost are a good choice and, for robustness, CNN and LSTM are the best choice. Ultimately, the choice of a model depends on the objectives of the analysis.
Authors: Maryamsadat Bagheri, Paolo Giudici
Institution: University of Pavia
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $3,550
OpenAlex ID: W4414224027