Нестационарность описывает data или features, статистические свойства которых меняются со временем или по другому измерению. Она важна в машинном обучении, поскольку models trained on one distribution may degrade when the real-world distribution shifts.
A feature whose values change across one or more dimensions, usually time. For example, consider the following examples of nonstationarity: - The number of swimsuits sold at a particular store varies with the season. - The quantity of a particular fruit harvested in a particular region is zero for much of the year but large for a brief period. - Due to climate change, annual mean temperatures are shifting. Contrast with stationarity.