Bitcoin Price Forecasting Based on Hybrid Variational Mode Decomposition and Long Short Term Memory Network
This addresses the problem of predicting volatile Bitcoin prices for financial analysts or traders, but it is incremental as it combines existing methods.
The study tackled Bitcoin price forecasting by proposing a hybrid model combining Variational Mode Decomposition (VMD) and Long Short-Term Memory (LSTM) networks, which outperformed standard LSTM across metrics like RMSE, MAE, and R2 and provided reliable 30-day forecasts.
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.