Wide-sense stationarity (WSS) refers to a stochastic process whose mean and variance are constant over time, and the covariance between values at two different times only depends on the time difference between them. This property allows for a simplification in the analysis of random processes, enabling easier prediction and understanding of their behavior. WSS plays a key role in areas like signal processing and time series analysis, making it easier to work with data that follows these patterns.
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