A stationary process is a stochastic process whose statistical properties do not change over time. This means that parameters like the mean, variance, and autocorrelation are constant regardless of when you observe the process. Stationarity is crucial for understanding long-term behavior and making predictions about future events based on past data, particularly when applying concepts like Kac's Lemma, which relates to return times in Markov chains.
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