Time series/ sequential data study group

[hwasiti (Haider Alwasiti)]

@oguiza

And what about multivariate time series?

There are several approaches you could follow.
In all cases you would first transform each individual TS within a MTS into an image (array).
Some alternatives are:

1. Tile images to build a larger image.
2. Stack all images into a multichannel (3+) array.
3. Fuse the images into a single image.
   To perform 2 & 3 you would need to modify the convnet to allow 3+ channels.

Thank you for the excellent post @oguiza
Here is an examples for (1):

1. This is something I am working one. Multiple EEG sensors on the scalp. So this is an example of multi-variate time series (each electrode position will pick up a signal that we are interested in its spectrograms, so we have 64 spectrograms). Have a look into the image with the black background :

I add to your list:
4. Concatenating multi CNN outputs (each CNN for each uni-variate spectrogram) into single dense NN like this:

How to remove the last fully connected layer from a CNN in fastai to stack more than one CNN into a FC-NN?

There are ML problems when we need to stack more than one model. Take for example chest X-Rays need stacking 2 CNNs for front and lateral views like this model : [ScreenClip] Source The above method is way better than stacking 2 full CNN into FC (which is I am aware it is doable with fastai) like this: [ScreenClip] Because the output of each CNN in the 1st image has way more features describing each xray image, which is crucially important… Since the physician will not be able to classif…