👋 Hello, team! When I try to pass static exogenous features to AutoNHITS by bellow code:

config_nhits = {
    "input_size": tune.choice([30, 30*2, 30*3]),              # Length of input window
    "start_padding_enabled": True,
    "n_blocks": 5*[1],                                              # Length of input window
    "mlp_units": 5 * [[64, 64]],                                  # Length of input window
    "n_pool_kernel_size": tune.choice([5*[1], 5*[2], 5*[4],         
                                      [8, 4, 2, 1, 1]]),            # MaxPooling Kernel size
    "n_freq_downsample": tune.choice([[8, 4, 2, 1, 1],
                                      [1, 1, 1, 1, 1]]),            # Interpolation expressivity ratios
    "learning_rate": tune.loguniform(1e-4, 1e-2),                   # Initial Learning rate
    "scaler_type": tune.choice(['robust']),                             # Scaler type
    "max_steps": tune.choice([1000,1500]),                               # Max number of training iterations
    "batch_size": tune.choice([1, 4, 10]),                          # Number of series in batch
    "windows_batch_size": tune.choice([128, 256, 512]),             # Number of windows in batch
    "random_seed": tune.randint(1, 20),                             # Random seed
    "stat_exog_list ": tune.choice([["L","AAM","ARC"],None]),
}

config_lstm = {
    "input_size": tune.choice([30, 30*2, 30*3]),              # Length of input window
    "encoder_hidden_size": tune.choice([64, 128]),            # Hidden size of LSTM cells
    "encoder_n_layers": tune.choice([2,4]),                   # Number of layers in LSTM
    "learning_rate": tune.loguniform(1e-4, 1e-2),             # Initial Learning rate
    "scaler_type": tune.choice(['robust']),                   # Scaler type
    "max_steps": tune.choice([800, 500]),                    # Max number of training iterations
    "batch_size": tune.choice([1, 4]),                        # Number of series in batch
    "random_seed": tune.randint(1, 20),                       # Random seed
    "stat_exog_list ": tune.choice([["L","AAM","ARC"],None]),
}

HORIZON = 30


nf = NeuralForecast(
    models=[
        AutoNHITS(h=HORIZON, config=config_nhits, loss=MQLoss(), num_samples=25),
        AutoLSTM(h=HORIZON, config=config_lstm, loss=MQLoss(), num_samples=20),
    ],
    freq='D'
)

nf.fit(df=train_set, static_df=static_df, val_size=30)

Then I get the error about unexpected keyword:

TypeError: Trainer.__init__() got an unexpected keyword argument 'stat_exog_list '

Hey! I think you have a small typo. There's a space after

stat_exog_list

. Otherwise, it should work 🙂

oh thanks a lot!!It works! I didn't notice it.