On TSDatasets getter RecursionError: maximum recursion depth exceeded about tsai HOT 6 CLOSED

Hi @free-soellingeraj,

I've looked at the error but it's a bit difficult to say exactly what it is without some additional data.
I cannot comment on the first part to get the X. It seems like a complicated process, but that is data preparation. In the end, if you are going to use SlidingWindow you need to prepare a pandas df with the required columns, in the format that you want, and without 'nan' values.
The important things I'd need to know are:

1. Could you please use check_data(X1, y1, splits1) and print out the output?
2. I'd also need you to create a batch and print out the following:

xb, yb = first(dls.train)
print(xb, yb)
print(xb.shape, yb.shape)

from tsai.

from tsai.

Great, thanks.
timesteps cannot be 1 as that means you don't have any sequences, just scalars. In your case, timesteps are determined by your window_len hyperparameter in SlidingWindow. You need to set that to the appropriate number depending on your task.

from tsai.

from tsai.

It’s an extreme case indeed. And you want be able to use any of the tsai models as they require time series/ sequences.
If you are trying to predict a value from another value you can use any of the sklearn models.
I have no idea what type of problem you are trying to tackle, but it makes sense to use different window lengths to really understand how much history is required to generate a good forecast. I don’t think there are good rules of thumb to define the window length, although you want yo capture seasonality if present. It depends on the task.
But what you can do to check that your data is well prepared is to set the window length to 30 for example, and then try to train a model.

from tsai.

from tsai.