ARIMA models, which stands for AutoRegressive Integrated Moving Average, are a class of statistical models used for analyzing and forecasting time series data. They combine three components: autoregression (AR), which uses past values to predict future ones; differencing (I), which helps stabilize the mean of the time series; and moving averages (MA), which model the relationship between an observation and a residual error from a moving average model. These models are particularly useful when data shows trends or seasonality. By identifying patterns in historical data, ARIMA models can provide insights and forecasts for various applications, such as economics, finance, and environmental studies.