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Hey @Manuel Chabier Escolá Pérez Here are two pointers that may help you. Hyperparameter optimization tutorial: https://nixtla.github.io/neuralforecast/examples/automatic_hyperparameter_tuning.html AutoNBEATSx code: https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/auto.py#L454

Thank you very much for your quick response, Kin. However, I still do not understand it... In the N-BEATSx class (link) I clearly see how to include the parameters (including

stat_exog_list

and

futr_exog_list

). However, when I read the AutoNBEATSx class (link) I do not see how or where these two parameters have to be indicated. The same with AutoNHITS. I get the error TypeError: AutoNHITS.__init__() got an unexpected keyword argument 'futr_exog_list'.

You have to put them in the configuration search space defined by the

config

, like this:

nbeatsx_config = {
       "max_steps": 100,                                                         # Number of SGD steps
       "input_size": 24,                                                         # Size of input window
       "learning_rate": tune.loguniform(1e-5, 1e-1),                             # Initial Learning rate
       "n_pool_kernel_size": tune.choice([[2, 2, 2], [16, 8, 1]]),               # MaxPool's Kernelsize
       "n_freq_downsample": tune.choice([[168, 24, 1], [24, 12, 1], [1, 1, 1]]), # Interpolation expressivity ratios
       "stat_exog_list": tune.choice([['s1','s2'],['s1']]),
       "futr_exog_list": tune.choice([['f1','f2'],['f1']]),
       "val_check_steps": 50,                                                    # Compute validation every 50 steps
       "random_seed": tune.randint(1, 10),                                       # Random seed
    }