Learn about autocorrelation, why it matters for prediction, and how to plot an autocorrelogram.

The autocorrelation coefficient

We say that a random variable yty_t is autocorrelated if its value at a period tt depends to some degree on previous realizations. In other words, a variable is autocorrelated if we can explain its value today using its lagged values from the past. This is equivalent to saying that the variable is serially correlated or that it presents a serial correlation.

The concept of serial correlation is intimately connected to the concept of weak stationarity. The autocorrelation coefficient, ρj\rho_j, is, in fact, a function of the autocovariance coefficient, γj\gamma_j.

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