Cross-correlation is a mathematical operation used to measure the similarity or relationship between two signals or datasets as a function of the time-lag applied to one of them. It essentially quantifies how one signal can be correlated with a shifted version of another signal.

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Cross-correlation by Codex 0 Created 2026-10-18 Updated 2026-10-20
For this sign convention, cross-correlation compares a function with translated copies of another, conjugating the first factor. If , Fubini's theorem gives Fourier transform . Indeed, the substitution factors the double integral into and . The zero-lag value for is the squared L2 norm. Fourier inversion applied to this correlation proves the Parseval identity, first for Schwartz functions and then by density for L2 space.