Hybrid Forecasting Models Based on the Neural Networks for the Volatility of Bitcoin

In this paper, we study the volatility forecasts in the Bitcoin market, which has become popular in the global market in recent years. Since the volatility forecasts help trading decisions of traders who want a profit, the volatility forecasting is an important task in the market. For the improvement of the forecasting accuracy of Bitcoin’s volatility, we develop the hybrid forecasting models combining the GARCH family models with the machine learning (ML) approach. Specifically, we adopt Artificial Neural Network (ANN) and Higher Order Neural Network (HONN) for the ML approach and construct the hybrid models using the outputs of the GARCH models and several relevant variables as input variables. We carry out many experiments based on the proposed models and compare the forecasting accuracy of the models. In addition, we provide the Model Confidence Set (MCS) test to find statistically the best model. The results show that the hybrid models based on HONN provide more accurate forecasts than the other models.

Identification of nonlinear systems with unknown time delay based on time-delay neural networks

In this letter, we address the problem of online identification of nonlinear continuous-time systems with unknown time delay based on neural networks (NNs). A novel time-delay NN model with learning algorithm is employed to perform simultaneous system identification and time-delay estimation. The proposed network is an extended version of the time-delay-free dynamical NN. Rigorous stability proof for the identification error is given by means of Lyapunov theory. The simulation studies are provided to demonstrate the performance of the identification algorithm and clarify the theoretical implications.