The article discusses autoregressive linear mixed effects models, which are a way to analyze data over time by looking at how the current response is related to the previous response, fixed effects, and random effects. These models can help predict future responses based on past data and current variables. The researchers provide different ways to represent these models and show how they can account for different types of errors and missing data. Overall, autoregressive linear mixed effects models are a useful tool for understanding and predicting trends in data over time.