ARIMA models are a class of statistical models used for analyzing and forecasting time series data by capturing various temporal structures, such as trends and seasonality, through differencing and autoregressive moving average components. These models are particularly useful for stationary time series, where the mean and variance are constant over time, and require careful parameter selection to optimize performance.