For those of you interested in the area of Time Series Classification, I’ve created a new repo called “Practical Deep Learning for Time Series” based on the fastai library.
It’s based on an idea I’ve been developing for quite some time. What I plan to do is to share a lot of code that I’ve created over the last few months, as well as some notebooks to demo how that code can be used. You will see that everything is focused on Time Series (Classification and Regression in particular).
The first commit of this repo contains the following:
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Fastai time series library called fast_timeseries. It contains lots of things I’ll be demoing in notebooks in the next few weeks. In the first one we’ll make use of custom TSItem, TSItemLists, TSDataBunch, etc. You’ll see that it makes the use of time series in fastai really easy.
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I’ve also included a pytorch model library called torchtimeseries.models. It contains some of the state-of-the-art models for time series classification (based on raw data). I’ve included FCN, ResNet, ResCNN and InceptionTime. I have other models, but I believe these work really well in small/ medium datasets. I’ll add more models in the near future.
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I’ve also created a first notebook (Intro to Time Series Classification) to demo how to integrate all this in a simple way, to that you may be able to create a state-of-the-art models in just a few minutes.
In future notebooks, I’ll try to explain how you can start using more advanced initialization schemes, data augmentation for time series, visualization techniques, and many other topics related to Time Series.
I’d love to receive some feeback, expecially if there’s anything that doesn’t work as expected, or is not clear, or is missing.