[BUG] Static covariates error when training global models on lists of TimeSeries

[rgrosskopf]

Describe the bug
A ValueError is thrown when attempting to train global models (I've tried LightGBMModel and LinearRegressionModel) on lists of TimeSeries objects that all have the same static covariates assigned.

The static covariate labels are strings and I've tried both with and without using StaticCovariatesTransformer() to convert the strings to float labels.

To Reproduce
All TimeSeries objects have the same structure but vary in length. For example:

AND

Fit the model:

model = LightGBMModel(lags=[-1] , output_chunk_length=1)
model.fit(all_train_scaled, future_covariates=None)

Error:

/local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.9/site-packages/darts/utils/data/tabularization.py in _add_static_covariates(model, series, features)
    219 
    220         # concatenate static covariates to features
--> 221         return np.concatenate([features, static_covs], axis=1)

/local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.9/site-packages/numpy/core/overrides.py in concatenate(*args, **kwargs)

ValueError: all the input array dimensions for the concatenation axis must match exactly, but along dimension 0, the array at index 0 has size 16571 and the array at index 1 has size 16570

Expected behavior
The static_covs array should match the dimensions of the features array so that they can be concatenated.

System (please complete the following information):

• Python version: 3.9.5
• darts version 0.23.0
• lightgbm version 3.3.2

Additional context
DARTS has been a huge boost for me. Thanks for all the effort, especially the effort put into the docs.

[eliane-maalouf]

thank you for bringing this up @rgrosskopf, I will have a look into the issue and keep you updated

[rgrosskopf]

Thanks so much for the quick turn around on the bug fix @eliane-maalouf. Handling that error was more complicated than I thought it would be.