A Comparative Study of Autoregressive and Neural Network Models: Forecasting the GARCH Process
Source of Publication
Lecture Notes in Computer Science
The Covid-19 pandemic has highlighted the importance of forecasting in managing public health. The two of the most commonly used approaches for time series forecasting methods are autoregressive (AR) and deep learning models (DL). While there exist a number of studies comparing the performance of AR and DL models in specific domains, there is no work that analyzes the two approaches in the general context of theoretically simulated time series. To fill the gap in the literature, we conduct an empirical study using different configurations of generalized autoregressive conditionally heteroskedastic (GARCH) time series. The results show that DL models can achieve a significant degree of accuracy in fitting and forecasting AR-GARCH time series. In particular, DL models outperform the AR-based models over a range of parameter values. However, the results are not consistent and depend on a number of factors including the DL architecture, AR-GARCH configuration, and parameter values. The study demonstrates that DL models can be an effective alternative to AR-based models in time series forecasting.
Springer International Publishing
Time series forecasting, Neural networks, Deep learning, ARIMA, GARCH
Kamalov, Firuz; Gurrib, Ikhlaas; Moussa, Sherif; and Nazir, Amril, "A Comparative Study of Autoregressive and Neural Network Models: Forecasting the GARCH Process" (2022). All Works. 5288.
Indexed in Scopus