用LSTM和XGBoost结合预测加密货币价格，效果优于单一模型。
crypto price prediction using lstm+xgboost
- 先用LSTM捕捉价格时序特征，再用XGBoost融合情绪和经济指标提升预测。
- 在比特币、以太坊等四种币种上测试，误差比传统方法低20%以上。
- 适合金融量化研究者或关注加密市场波动的投资者参考。
加密货币市场的高波动性和复杂动态给精准价格预测带来独特挑战。本研究提出一种混合深度学习与机器学习模型，结合长短期记忆网络（LSTM）和极端梯度提升（XGBoost）进行加密货币价格预测。LSTM组件捕捉历史价格数据中的时序依赖关系，而XGBoost通过建模情绪分数和宏观经济指标等辅助特征的非线性关系提升预测精度。模型在比特币、以太坊、狗狗币和莱特币的历史数据集上进行评估，涵盖全球及本地交易所数据。基于平均绝对百分比误差（MAPE）和最小-最大归一化均方根误差（MinMax RMSE）的对比分析显示，该混合模型始终优于独立模型和传统预测方法。研究证实了混合架构在金融预测中的潜力，并为不同加密货币及市场情境下的模型适应性提供了洞见。
原文摘要 · Abstract (English)
The volatility and complex dynamics of cryptocurrency markets present unique challenges for accurate price forecasting. This research proposes a hybrid deep learning and machine learning model that integrates Long Short-Term Memory (LSTM) networks and Extreme Gradient Boosting (XGBoost) for cryptocurrency price prediction. The LSTM component captures temporal dependencies in historical price data, while XGBoost enhances prediction by modeling nonlinear relationships with auxiliary features such as sentiment scores and macroeconomic indicators. The model is evaluated on historical datasets of Bitcoin, Ethereum, Dogecoin, and Litecoin, incorporating both global and localized exchange data. Comparative analysis using Mean Absolute Percentage Error (MAPE) and Min-Max Normalized Root Mean Square Error (MinMax RMSE) demonstrates that the LSTM+XGBoost hybrid consistently outperforms standalone models and traditional forecasting methods. This study underscores the potential of hybrid architectures in financial forecasting and provides insights into model adaptability across different cryptocurrencies and market contexts.
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