Stationarity
Stationarity is a statistical property of a time series where its statistical characteristics, such as mean, variance, and autocorrelation, remain constant over time. This concept is fundamental in time series analysis, as many forecasting models, like ARIMA, assume stationarity to make reliable predictions. It ensures that the underlying data-generating process does not change, allowing for consistent modeling and inference.
Developers should learn stationarity when working with time series data in fields like finance, economics, or IoT, as it is a prerequisite for applying models like ARIMA, which require stationary data to avoid spurious results. It is used in scenarios such as stock price forecasting, weather prediction, or anomaly detection, where understanding data stability over time is crucial for accurate analysis and decision-making.