Wavelet transforms are mathematical tools used to analyze time-series data. Unlike traditional Fourier transforms, wavelet transforms can decompose a signal into both time and frequency components, providing a more localized and detailed view of the data. This makes them particularly useful for analyzing non-stationary signals—those whose statistical properties change over time.
