用变分模态分解+LSTM预测比特币价格，效果优于传统模型
Bitcoin Price Forecasting Based on Hybrid Variational Mode Decomposition and Long Short Term Memory Network
- 先用VMD分解价格序列，再用LSTM分别建模各分量
- 在RMSE、MAE、R2三项指标上均优于纯LSTM模型
- 适合关注加密货币短期走势的量化投资者
本研究提出一种混合深度学习模型用于预测比特币价格，因其波动频繁而具挑战性。采用变分模态分解（VMD）将原始比特币价格序列分解为固有模态函数（IMFs），再对每个IMF使用长短期记忆网络（LSTM）捕捉时间模式。各分量的预测结果聚合后生成原价格序列的最终预测。通过与标准LSTM对比，验证了混合VMD+LSTM模型在所有评估指标（包括RMSE、MAE和R2）上均表现更优，并能提供可靠的30天预测。
原文摘要 · Abstract (English)
This study proposes a hybrid deep learning model for forecasting the price of Bitcoin, as the digital currency is known to exhibit frequent fluctuations. The models used are the Variational Mode Decomposition (VMD) and the Long Short-Term Memory (LSTM) network. First, VMD is used to decompose the original Bitcoin price series into Intrinsic Mode Functions (IMFs). Each IMF is then modeled using an LSTM network to capture temporal patterns more effectively. The individual forecasts from the IMFs are aggregated to produce the final prediction of the original Bitcoin Price Series. To determine the prediction power of the proposed hybrid model, a comparative analysis was conducted against the standard LSTM. The results confirmed that the hybrid VMD+LSTM model outperforms the standard LSTM across all the evaluation metrics, including RMSE, MAE and R2 and also provides a reliable 30-day forecast.
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