The statistics seminar this week will be a session for Data Science honour presentation. The seminar/presentation will last 30 minutes. A comparison of hybrid ARIMA-GARCH and ARIMA-ANN on time series forecasting Speaker: Yunlin Peng Location: AGR 829 Zoom link: https://uni-sydney.zoom.us/j/82618524792 Due to a broader range of applications in time series applications in different domains, especially in finance, accurate forecasting is crucial in decision-making. Autoregressive Integrated Moving Average (ARIMA) model and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model are popular statistical time series models for modelling the linearity and volatility in time series. In recent years, Artificial Neural Networks (ANNs) have been implemented in time series forecasting due to its ability of capturing nonlinearity in data. Despite regularly high accuracy of these three models, complex real-world data is less likely to contain pure linear or nonlinear patterns, which significantly restricts the performance of these models. Hence, hybrid models combining linear and nonlinear models by utilizing each modelâs advantages have been investigated. In this project, performances of individual models, ARIMA and ANN, and hybrid models, ARIMA-GARCH, additive ARIMA-ANN, andÂ multiplicative ARIMA-ANN, will be compared on various datasets with different characteristics. The empirical results indicate that hybrid models based on ARIMA-ANN outperform other models, and the multiplicative ARIMA-ANN enhances the forecast accuracy on mixed-linearity data the most.