Stationarity
In data science, stationarity refers to the statistical properties of a time series that remain constant over time. A stationary time series is one whose mean, variance, and autocorrelation structure do not change over time. Stationarity is an important concept in time series analysis because many forecasting models assume that the time series is stationary. Non-stationary time series can be transformed into stationary time series through techniques such as differencing and detrending. Stationarity is also important in machine learning, where it is used to ensure that the training and test data have similar statistical properties.
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