Publikation
Echo State Networks for Bitcoin Time Series Prediction
Mansi Sharma; Enrico Sartor; Marc Cavazza; Helmut Prendinger
In: International Conference on Neural Information Processing. International Conference on Neural Information Processing (ICONIP), Pages 365-380, Springer, 2025.
Zusammenfassung
Forecasting stock and cryptocurrency prices is challenging
due to high volatility and non-stationarity, influenced by factors like
economic changes and market sentiment. Previous research shows that
Echo State Networks (ESNs) can effectively model short-term stock market movements, capturing nonlinear patterns in dynamic data. To the
best of our knowledge, this work is among the first to explore ESNs for
cryptocurrency forecasting, especially during extreme volatility. We also
conduct chaos analysis through the Lyapunov exponent in chaotic periods and show that our approach outperforms existing machine learning methods by a significant margin. Our findings are consistent with
the Lyapunov exponent analysis, showing that ESNs are robust during
chaotic periods and excel under high chaos compared to Boosting and
Naïve methods.
