@ourownstory, welcome to the fastai community.
I will try to answer your question. There are different approaches that have been used to process time series data using deep learning. Time series can be divided in 3 categories:
Time series Classification/Regression : These categories can be put under the same umbrella as they share some common background. For these 2 categories, time series can be treated either as a tabular data or 2D tensors similar to 3D tensors used in images (as expressed in `fastai v2’ module ).
For the tabular approach, you can check both @oguiza TimeseriesAI repo (using fastai v1) and @tcapelle timeseries_fastai (using fastai v2).
For the 2D tensor approach, you may check timeseries (using fastai v2). In this approach, I draw a similarity between TensorImage (fastai v2 native class) and Tensor2D (that I introduced in the timeseries module). In fact, we have the following mapping:
TensorImage <---> TensorTS
Conv2D <---> Conv1D
Time series Forecasting is like a separate category. Lately, there is a lot of research that has been published in this domain. It seems that LSTM is one the popular approach that showed some strong results. Time series forecasting benefits from the LSTM architecture as it inherently takes into the sequentiality of data in a similar way found while training a language model (predicting the next word or the next sequence of words).
You may also check @takotab time series forecasting module fastseq that he implemented in fastai v2.
I hope this information will give you a kind of summary of the different modules (that I’m aware of) developed using fastai v1 and v2 for time-series processing using deep learning.