Wavelet transformation to analyse time series

1. What is wavelet analysis for time series?
2. What is wavelet transform used for?
3. Is wavelet a time-frequency analysis?
4. In what way wavelet transform is better than Fourier transform?

What is wavelet analysis for time series?

Wavelet analysis is a useful supplementary technique for analysing time series, in particular for transient and chirped signals involving different wave modes and harmonics. Some basic wavelet properties are summarized, and wavelet analysis of simple signals are presented.

What is wavelet transform used for?

The wavelet transform (WT) can be used to analyze signals in time–frequency space and reduce noise, while retaining the important components in the original signals. In the past 20 years, WT has become a very effective tool in signal processing.

Is wavelet a time-frequency analysis?

The continuous wavelet transform (CWT) is a time-frequency transform, which is ideal for analyzing nonstationary signals. A signal being nonstationary means that its frequency-domain representation changes over time.

In what way wavelet transform is better than Fourier transform?

While the Fourier transform creates a representation of the signal in the frequency domain, the wavelet transform creates a representation of the signal in both the time and frequency domain, thereby allowing efficient access of localized information about the signal.