Hi there, I met a small issue when I trying to induce the exogenous variables into the AutoModel with HypterParameter tuning. Let's say, made a historical exogenous variable called 'x_7' "hist_exog_list": tune.choice(['x_7'])

### NBeatsx HyperParameter Config
nbeatsx_config = {
       "input_size": tune.choice([1 * input_sequence]),                  # input_size = multiplier * horizon
       "step_size": tune.choice([4]),                            # Window shift size
       "hist_exog_list": tune.choice(['x_7']), #df.columns[1:-2].tolist(),
}

And put the config to the model iniation then fit the trian input

NBeatsx = AutoNBEATSx(h=output_sequence, config=nbeatsx_config, loss=MAE(), gpus=1, search_alg=HyperOptSearch(), backend='ray', num_samples=40)
nf = NeuralForecast(models=[NBeatsx], freq='H', local_scaler_type='standard')
nf.fit(Y_train_df, val_size=val_size, verbose=True)

Then, the training session failed with the output error

File "/usr/local/lib/python3.10/dist-packages/neuralforecast/common/_base_auto.py", line 336, in _fit_model
    model.fit(dataset, val_size=val_size, test_size=test_size)
  File "/usr/local/lib/python3.10/dist-packages/neuralforecast/common/_base_windows.py", line 697, in fit
    raise Exception(
Exception: {'7', 'x', '_'} historical exogenous variables not found in input dataset

Anyone could please explain this? Also, one extra question is that, If I am going to have about 700 exogenous variables as the input, is it OKAY/BAD to have huge exogenous dimensions. Thanks!

Hey. The features must be a list, and the tune choice expects a sequence, so what's happening is that you're providing

'hist_exog_list': 'x_7'

and it should be

'hist_exog_list': ['x_7']

so the config for tune should be something like:

hist_exog_list: tune.choice([['x_7']])

About the 700 exogenous the problem is memory, so it's really up to the hardware, but i'd say it's probably going to run out of memory.

Thanks for the quick replying! After I changing to the 'list' as you shown above, the new Exception occurs:

(_train_tune pid=2432)   File "/usr/local/lib/python3.10/dist-packages/neuralforecast/common/_base_windows.py", line 336, in _get_temporal_data_cols
(_train_tune pid=2432)     set(temporal_cols.tolist()) & set(self.hist_exog_list + self.futr_exog_list)
(_train_tune pid=2432) TypeError: can only concatenate tuple (not "list") to tuple

The value I used is pure integer value.

Did you provide it like I suggested? Seems like the hist_exog_list is a tuple instead of a list

@王梦石, would you not want to do feature selection first before you feed into the nfc models since you have 700 exogenous features?

Hi Jose, I did exactly as your suggestion. If I do not specify the 'futr_exog_list' as an empty list, the error still exist there. It's kinda weird

I think this is what it does internally, so if you specify it as a list it should return it as a list