Bitcoin Price Forecasting Based on Hybrid Variational Mode Decomposition and Long Short Term Memory Network
Emmanuel Boadi
这项研究提出了一种用于预测比特币价格的混合深度学习模型,因为众所周知,数字货币表现出频繁的波动。 所使用的模型是变频模式分解(VMD)和长短期内存(LSTM)网络。 首先,VMD用于将原始比特币价格序列分解为内在模式功能(IMF)。 然后,每个IMF都使用LSTM网络建模,以更有效地捕获时间模式。 货币基金的个人预测汇总,以产生原始比特币价格系列的最终预测。 为了确定拟议混合模型的预测功率,对标准LSTM进行了比较分析。 结果证实,混合VMD+LSTM模型在所有评估指标(包括RMSE,MAE和R2)中均优于标准LSTM,还提供了可靠的30天预测。
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 a...
