Traditional methods like Fourier Transformation are effective for stationary signals, but they fall short when dealing with non-stationary data, which is common in epidemiological studies. Wavelet transformation allows for the decomposition of data into different frequency components, making it easier to analyze changes over time. This technique is particularly useful for understanding the impact of seasonal variations, outbreaks, and other time-dependent phenomena.
