stationary

In statistics and time series analysis, "stationary" describes a process whose statistical properties, such as mean, variance, and correlation, remain consistent over time. This means the data's behavior doesn't change unpredictably; patterns observed in one period tend to repeat in others. Stationarity is important because it allows for more reliable modeling and forecasting, as stable underlying characteristics make predictions more accurate. Non-stationary data, which may have trends or seasonal effects, often require transformation before analysis to achieve stationarity.