Autoregressive models are statistical tools used in time series analysis to predict future values based on past observations. In these models, the current value of a variable is regressed on its previous values, allowing for the identification of patterns over time. This approach is commonly used in fields like economics and finance to forecast trends. A key feature of autoregressive models is their reliance on the assumption that past values contain useful information for predicting future outcomes. For example, in a stock market analysis, an autoregressive model might use the last few days' prices to estimate the price for the next day.