Echo State Networks for Bitcoin Time Series Prediction
Mansi Sharma, Enrico Sartor, Marc Cavazza, Helmut Prendinger

TL;DR
This paper explores the use of Echo State Networks for predicting Bitcoin prices, demonstrating their robustness and superior performance during periods of extreme market volatility and chaos.
Contribution
It is among the first studies to apply ESNs to cryptocurrency forecasting, especially during chaotic market conditions, and compares their effectiveness to other machine learning methods.
Findings
ESNs outperform existing methods in volatile periods
Lyapunov exponent analysis confirms ESN robustness during chaos
ESNs effectively model nonlinear patterns in cryptocurrency data
Abstract
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 Na\"ive methods.
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