A generalized additive time series model is an extension of the Generalized Additive Model (GAM) specifically designed for time series data. It incorporates time-dependent components, such as seasonality and trends, into the model to capture the temporal structure of the data. This allows for more accurate predictions and better understanding of the underlying patterns in time series data.