Bitcoin Volatility Forecasting Based on Time Series Decomposition and Deep Learning Model

Abstract

In recent years, various digital currencies have emerged, among which Bitcoin has been widely accepted as an alternative to sovereign currencies for commodity trading. However, the dramatic volatility of bitcoin prices can pose a risk to global financial markets. In this paper, we firstly construct a more comprehensive forecasting index system from seven aspects, and then construct a VMD-GRU model. This model uses the variational modal decomposition (VMD) to decompose the time series into intrinsic mode functions (IMFs) and use gated recurrent unit (GRU) to forecast different IMFs. This paper also compares the forecast results with classical machine learning models and deep learning models, and the results show that the forecast accuracy of the VMD-GRU model is more than 16% better than other models.