Weak Stationarity
Weak stationarity, also known as covariance stationarity or second-order stationarity, is a statistical property of a time series where the mean, variance, and autocovariance are constant over time. This means that the statistical properties of the series do not depend on the specific time point, making it easier to model and analyze using techniques like ARIMA models. It is a fundamental assumption in many time series analysis methods, ensuring that patterns are consistent and predictable.
Developers should learn weak stationarity when working with time series data in fields like finance, economics, or IoT, as it is a prerequisite for applying standard forecasting models such as ARIMA, which require stable statistical properties to make accurate predictions. It is used to check if data transformations (e.g., differencing or logging) are needed to achieve stationarity before modeling, helping avoid spurious results in applications like stock price analysis or sensor data monitoring.