Autocorrelation, also known as serial correlation, refers to the degree of similarity between a given time series and a lagged version of itself over successive time intervals. It measures the relationship between a variable’s current value and its past values.
On the other hand, stationarity is a statistical concept that a process or series has properties that do not depend on time. Specifically, it means that the mean, variance, and autocorrelation structure do not change over time.
The connection between these two concepts lies in their implications for data analysis. A stationary time series will have an autocorrelation function that drops to zero relatively quickly, while the autocorrelation function of a non-stationary series decreases slowly. Also, non-stationary data can lead to spurious results in certain analyses.
