Very good thread! Very interesting to learn about using CNNs to classify time-series from images, will definitely give that a go myself.
Personally I work with financial data through my institution and cannot share any of the datasets, but since watching v2 of this course some time ago I have adopted Jeremy’s ULMFiT approach to time-series classification. Briefly, the idea behind ULMFiT is before training a classifier (on some NLP task), one trains a language model (essentially a forecaster, a model which takes a sequence as input and tries to predict the next item in the sequence). Once the LM is strong enough, one slaps on a linear classifier to the end and fine tunes it for the classification task.
This method works exactly the same way for time series and I have used the method for improving classifiers that were trained from scratch in the domain of risk management (probability of default on loan, data consists of individuals deposit activity); asset pricing, and cash-flow optimization. I will try to take a look at some of those datasets in the link @oguiza was kind enough to share and put together a notebook illustrating the procedure.
I think it would be interesting if we try to put together a notebook as a team that compares some of these different methods:
i) CNN classification of un-altered time-series image
ii) CNN classification of transformed time-series image (Gramian Angular Field or maybe a 2d plot in time-frequency domain after wavelet transform… just a thought, see https://www.mathworks.com/examples/wavelet/mw/wavelet-ex11554099-continuous-wavelet-analysis for an example of what this might looks like)
iii) Direct RNN-based approach without ULMFiT pretraining
iv) RNN-based approach with ULMFiT
v) CNN based approach directly on time series with 1d convolution along the temporal dimension
Happy to add more to the list! A good place to start would be agreeing on one or two datasets/tasks and implementing one (or more) of the above methods and sharing a jupyter notebook here, then as we go someone can combine all these into one notebook and once there we an use this to quickly pit these methods against each other on wide range of tasks.