用回声状态网络预测比特币波动行情，效果优于传统方法。
Echo State Networks for Bitcoin Time Series Prediction
- 用回声状态网络建模比特币价格非线性动态变化
- 在极端波动期误差比提升方法低27.3%以上
- 适合关注加密货币短期预测的研究者
由于高波动性和非平稳性，股票与加密货币价格预测极具挑战，受经济变化和市场情绪影响。以往研究表明回声状态网络（ESNs）能有效捕捉短期股市变动中的非线性模式。据我们所知，这是首次将ESN应用于加密货币预测，特别是在极端波动时期。通过李雅普诺夫指数进行混沌分析，结果表明该方法显著优于现有机器学习模型。研究发现，当系统处于混沌状态时，ESN表现更优，相比提升法和朴素法具有更强鲁棒性。
原文摘要 · Abstract (English)
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.
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