[Explainer] Additional Features with the Stationary Wavelet Transform (SWT)

Hello everyone, I was previously a Senior Geophysicist for a Seismic Processing company and I now work in Deep Reinforcement Learning for robotics. I find this competition really interesting as it combines my previous experience in Geophysics with my current area of research, Deep Neural Networks.

Here in this Colab Notebook I demonstrate the use of the Stationary Wavelet Transform (SWT) to perform a frequency based decomposition (filter banks) on the input time series data. For my submission I have used this as input to a UNET architecture, written in PyTorch. I would be happy to share the UNET implementation if this post gets some attention.

Best of luck with the competition!

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