Feature extraction - wavelet transformation + autoregression I am working on a feature extraction problem (ECG signal). Within my literature review I stumbled across the following text:

"The wavelet transform is used to extract the coefficients of the transform as
the features of each ECG segment. Simultaneously, autoregressive
modelling(AR) is also applied to obtain the temporal structures
of ECG waveforms." here

My question now is, how do the wavelet transformation and the AR differ, i.e. how does the AR 'obtain the temporal structure'?
The SVM is used after the wavelet transform. The wavelet transform is a way to detect correlations between the parts/segments of the signal. Once you have these 'features' you can train more or less any classifier on that data.