can't generate dataset "pts_m5"

[binaryiii]

Excuse me，when i run the code like this: dataset = get_dataset("pts_m5", regenerate=True) there was an error like: RuntimeError: M5 data is available on Kaggle (https://www.kaggle.com/c/m5-forecasting-accuracy/data). You first need to agree to the terms of the competition before being able to download the data. After you have done that, please copy the files into C:\Users\bzn.mxnet\gluon-ts\datasets\pts_m5. But i have copied the files into C:\Users\bzn.mxnet\gluon-ts\datasets\pts_m5.

[kashif]

and can you try:

dataset = get_dataset("m5", regenerate=True)

[binaryiii]

dataset = get_dataset("m5", regenerate=True) Yes! I tried that in the Implicit-Quantile-Network-Example.ipynb and run the code successfully. But in the m5.ipynb and m5-tft.ipynb ,if i try dataset = get_dataset("m5", regenerate=False) the result will be very different from the initial result in ipynb.

[Usama-Samad]

Getting the same issue, even after copying the files and also putting the regenerate to True. getting this error FileNotFoundError: [Errno 2] No such file or directory: '/home/abdul/.mxnet/gluon-ts/datasets/m5/metadata.json' dont know how to get this metadata file.

[kashif]

can you make sure all these files in the folder:

https://github.com/zalandoresearch/pytorch-ts/blob/master/pts/dataset/repository/_m5.py#L22C6-L24C57

[hanlaoshi]

Excuse me， Dr. Kashif, I noticed in your publicly available code that the following code is used: dataset = get_dataset("traffic_nips", regenerate=True) The training time steps for the "traffic_nips" dataset is set to 4001 in your code. However, in the "Conditioned Normalizing Flow" paper, it is mentioned that the time step for this dataset is 10,413. On the other hand, the "TimeGrad" paper indicates a time step of 4001. Could you please clarify how the dataset with a time step of 10,413 was obtained?

Here is the example of pytorch-ts-0.7.0 https://github.com/zalandoresearch/pytorch-ts/blob/version-0.7.0/examples/Traffic.ipynb