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.

翻译：加密货币市场的波动性与复杂动态特性为精准价格预测带来了独特挑战。本研究提出一种融合长短期记忆网络与极限梯度提升的混合深度学习与机器学习模型，用于加密货币价格预测。LSTM组件捕捉历史价格数据中的时序依赖关系，而XGBoost通过建模情感评分和宏观经济指标等辅助特征的非线性关系来增强预测性能。该模型在比特币、以太坊、狗狗币和莱特币的历史数据集上进行评估，同时整合全球与本地化交易所数据。采用平均绝对百分比误差和最小-最大归一化均方根误差的对比分析表明，LSTM+XGBoost混合模型始终优于独立模型与传统预测方法。本研究凸显了混合架构在金融预测领域的潜力，并为模型在不同加密货币与市场环境中的适应性提供了见解。