Final answer:
Autocorrelation measures the similarity between a signal and its delayed version, whereas FFT converts a signal into its frequency domain for spectral analysis.
Step-by-step explanation:
The difference between autocorrelation and Fast Fourier Transform (FFT) involves their functions and applications in signal processing and time series analysis. Autocorrelation is a mathematical tool used to measure the similarity between a signal and a delayed version of itself over varying time lags. It is typically used to find repeating patterns or periodic signals within a dataset and to identify the presence of a trend or seasonal variation.
On the other hand, the Fast Fourier Transform is an algorithm to compute the Discrete Fourier Transform (DFT) of a sequence, or its inverse, efficiently. FFT converts a signal from its original time or space domain into a representation in the frequency domain. This transformation allows for the analysis of the signal's frequency content and is instrumental in spectral analysis, filtering, and data compression.
Simply put, while autocorrelation measures correlation over time, the FFT analyzes the frequency spectrum of a signal.