AutoRegressive (AR) models are statistical tools used in time series analysis to predict future values based on past observations. In an AR model, the current value of a variable is expressed as a linear combination of its previous values, allowing for the identification of patterns over time. This approach is particularly useful in fields like economics and finance, where understanding trends is crucial. The AR model is often part of a broader class of models known as ARIMA, which combines AutoRegressive components with moving averages and differencing. By analyzing historical data, AR models help forecast future trends, making them valuable for decision-making in various industries.