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There are a lot of opportunities for more augmentation functions:
tk.augment_logrithmic()
tk.augment_polynomial()
tk.augment_hilbert()
tk.augment_wavelet()
tk.augment_short_fourier() <- This is different than the normal fourier transform in that it breaks a signal into smaller segments to provide a time-varying analysis with adjustable time and frequency resolutions.
These are just a few, but all represent further oppotunities to try and add valuable information that has historically been leveraged in the extant time series and signal processing literature.
The text was updated successfully, but these errors were encountered:
Per @JustinKurland:
There are a lot of opportunities for more augmentation functions:
tk.augment_logrithmic()
tk.augment_polynomial()
tk.augment_hilbert()
tk.augment_wavelet()
tk.augment_short_fourier()
<- This is different than the normal fourier transform in that it breaks a signal into smaller segments to provide a time-varying analysis with adjustable time and frequency resolutions.These are just a few, but all represent further oppotunities to try and add valuable information that has historically been leveraged in the extant time series and signal processing literature.
The text was updated successfully, but these errors were encountered: