Echo State Networks for Bitcoin Time Series Prediction

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.