Auto-correlation is a mathematical tool used to measure the similarity between a signal and a time-shifted version of itself over different intervals of time. It plays an important role in identifying repeating patterns or periodic signals within data, which is crucial for analyzing systems and signals in various applications. Auto-correlation is fundamentally linked to convolution, as both processes deal with the interaction of signals, but while convolution focuses on the influence of one signal over another, auto-correlation assesses the self-similarity of a single signal over time.
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