Lag in autocorrelation refers to the time difference between two points in a time series. It’s used to measure the correlation of a variable with itself at different intervals, or lags. For instance, if we have daily temperature data and set a lag of 1, we’re comparing each day’s temperature with the previous day’s. If the lag is 2, we compare it with the temperature from two days ago. Autocorrelation can reveal patterns like seasonality by showing how similar today’s value is to past values. High autocorrelation at certain lags may indicate a repeating pattern over that interval.
