What Can Wavelet Transforms Be Applied for

Wavelets transforms can be used for noise removal, detection of abrupt discontinuities, and many more

Wavelet Transforms (WT) or wavelet analysis is probably the most recent solution to overcome the shortcomings of the Fourier Transform (FT). WT transforms a signal in period (or frequency) without losing time resolution. In the signal processing context, WT provides a method to decompose an input signal of interest into a set of elementary waveforms, i.e. “wavelets”., and then analyze the signal by examining the coefficients (or weights) of these wavelets. In this post, we will introduce where the wavelet transforms can be used.

1. Wavelet Transform Applications

In general, wavelets transforms can be used for stationary and nonstationary signals, including but not limited to the following:

• noise removal from the signals
• trend analysis and forecasting
• detection of abrupt discontinuities, change, or abnormal behavior, etc.
• compression of large amounts of data
• data encryption, i.e. secure the data
• Combine it with machine learning to improve the modelling accuracy

2. Some Examples (1) Noise removal and trend analysis