Autocorrelation, in signal processing, is a mathematical tool used to identify repeating patterns or periodicity within a signal. It measures the linear relationship between time-lagged values of the same variable, providing insights into the properties of the signal such as its stability and predictability. Autocorrelation can reveal hidden periodic components not immediately apparent in raw data. In addition, it’s crucial for understanding the spectral density of signals, which helps in designing filters and predicting future signal values.
