Stationary models are mathematical representations used in various fields, such as statistics and economics, where the underlying processes do not change over time. In these models, key properties, like mean and variance, remain constant, allowing for easier analysis and predictions. This stability makes stationary models particularly useful for understanding long-term trends and behaviors in data. In time series analysis, for example, stationary models help identify patterns without the influence of external factors that may cause fluctuations. Common techniques include the Autoregressive Integrated Moving Average (ARIMA) model, which is widely used for forecasting and analyzing time-dependent data while assuming stationarity in the underlying process.