autokorelace
Autokorelace, also known as autocorrelation or serial correlation in statistics, is a measure of the relationship between observations in a time series and past values of the same series. It quantifies how the current value of a process relates to its past values at different lags, helping to reveal patterns such as persistence, seasonality, or structure in the data.
Mathematically, for a stochastic process Xt with mean μ, the autocovariance at lag k is gamma(k) = E[(Xt
In practice, autocorrelation is estimated from data as the sample autocorrelation. For a series {xt} of length
Common cautions include interpreting autocorrelation as causation, distinguishing true dependence from trends or seasonality, and ensuring