Attention based hybrid parametric and neural network models for non-stationary time series prediction

This paper investigates non-stationary time series analysis and forecasting techniques for financial datasets. We focus on the use of a popular non-stationary parametric model namely GARCH and neural network model LSTM, with an attention mechanism to capture the complex temporal dynamics and dependencies in the data. We propose a hybrid GARCH-ATT-LSTM model where the GARCH model is employed for volatility forecasting, attention mechanism is applied to capture the more important parts of the data sequence and enhance the interpretability of the model, and the LSTM model is used for price forecasting. Our experiments are conducted on real-world financial datasets, that is, Apple stock price, Dow Jones index, and gold futures price. We compare the performance of GARCH-ATT-LSTM against the sole LSTM model, ATT-LSTM model, and LSTM-GARCH model. Our results show that GARCH-ATT-LSTM outperforms the baseline methods and achieves high accuracy in price forecasting. It implies the effectiveness of the attention mechanism in improving the interpretability and stability of the model and the success of combining parametric models with neural network models. The findings suggest that GARCH-ATT-LSTM can be a valuable tool for non-stationary time series analysis and forecasting in financial applications.

Automated summary

This paper investigates non-stationary time series analysis and forecasting techniques for financial datasets. A hybrid model, GARCH-ATT-LSTM, is proposed to improve the accuracy of price forecasting. Experimental results show that GARCH-ATT-LSTM outperforms other models, indicating the success of combining parametric models with neural network models.