Practical Python Wavelet Transforms (II): 1D DWT

The 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.

Wavelets transform can be used for stationary and nonstationary signals, including but not limited to the following:

noise removal from the signals

trend analysis and forecationg

detection of abrupt discontinuities, change, or abnormal behavior, etc. and

compression of large amounts of data

the new image compression standard called JPEG2000 is fully based on wavelets

data encryption,i.e. secure the data

Combine it with machine learning to improve the modelling accuracy

Therefore, it would be great for your future development if you could learn this great tool.  Practiclal Python Wavelet Transforms includes a series of courses, in which one can learn Wavelet Transforms using word–real cases. The topics of  this course series includes the following topics:

Part (I): Fundmentals

Part (II): 1D Discrete Wavelet Transform (DWT)

Sationary Wavelet Transform (SWT)

Multiresolutiom Analysis (MRA)