Autoregressive models are statistical tools used for analyzing time series data. They predict future values based on past observations, assuming that current values are influenced by their previous values. This approach is commonly used in fields like economics and finance to forecast trends and patterns. In an autoregressive model, the relationship between the current value and its past values is expressed through a mathematical equation. The model is often denoted as AR(p), where p represents the number of lagged observations included. These models help in understanding the dynamics of data over time and can be essential for decision-making processes.