AssertionError: filters should not remove entries all entries - check encoder/decoder lengths and lags

[MrGG14]

Im working on making predicctions for the next 24 hours of the energy price. The problem is i dont undestand how the datasets are created. I want to predict the next 24 values, i dont care if it´s just by using the previous 24 hours or more. I just dont know how to configure it.

All help is aprecciated. This is my dataframe

	fechaHora	precio_spot	demanda	co2	precio_gas	prod_eolica	prod_solar	demanda_residual	rampa	month	week	index	day
0	2022-08-31 00:00:00	227.82	26649.83	80.28	204.32	6061.25	232.17	21193.03	1750.55	0	0	0	0
1	2022-08-31 01:00:00	195.00	25480.42	80.28	204.32	5636.58	145.58	19582.33	1610.70	0	0	1	0
2	2022-08-31 02:00:00	184.95	24435.00	80.28	204.32	4902.33	113.83	18842.22	740.10	0	0	2	0
3	2022-08-31 03:00:00	181.69	23810.17	80.28	204.32	4227.33	62.58	19194.65	-352.43	0	0	3	0
4	2022-08-31 04:00:00	181.79	23520.92	80.28	204.32	3933.25	18.33	19861.95	-667.30	0	0	4	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...
13676	2024-03-22 19:00:00	57.30	29866.00	59.65	26.43	5725.08	257.67	23398.45	-3844.93	19	82	13676	569
13677	2024-03-22 20:00:00	53.92	30719.25	59.65	26.43	6607.83	80.17	24598.68	-1200.23	19	82	13677	569
13678	2024-03-22 21:00:00	35.00	29757.92	59.65	26.43	7931.58	64.00	22840.10	1758.58	19	82	13678	569
13679	2024-03-22 22:00:00	29.16	27183.58	59.65	26.43	9437.92	64.00	18698.83	4141.28	19	82	13679	569
13680	2024-03-22 23:00:00	15.60	24874.83	59.65	26.43	10415.50	63.00	14931.05	3767.78	19	82	13680	569

Expected behavior

Create training, validation and test data.

Actual behavior

AssertionError: filters should not remove entries all entries - check encoder/decoder lengths and lags

Code to reproduce the problem

features = [col for col in data.columns if col != 'precio_spot' ]  # Columnas de características and col != 'fechaHora'

max_prediction_length = 24
max_encoder_length = 24 #48
# training_cutoff = data["fechaHora"].max() - pd.Timedelta(hours=max_encoder_length)

training = TimeSeriesDataSet(
    train_data,
    time_idx="index",
    target="precio_spot",
    group_ids=["day"],
    min_encoder_length=24,  # keep encoder length long (as it is in the validation set)
    max_encoder_length=max_encoder_length,
    min_prediction_length=24,
    max_prediction_length=max_prediction_length,
    static_categoricals=[],
    static_reals=[],
    time_varying_known_categoricals=[], # group of categorical variables can be treated as one variable
    time_varying_known_reals=features,
    time_varying_unknown_categoricals=[],
    time_varying_unknown_reals=[],
    target_normalizer=GroupNormalizer(
        groups=["day"], transformation="softplus"
    ),
  
   # use softplus and normalize by group
    add_relative_time_idx=True,
    add_target_scales=True,
    add_encoder_length=True,
    categorical_encoders={
        'month':pytorch_forecasting.data.encoders.NaNLabelEncoder(add_nan=True),
        'week':pytorch_forecasting.data.encoders.NaNLabelEncoder(add_nan=True),
        'day':pytorch_forecasting.data.encoders.NaNLabelEncoder(add_nan=True),
        
    },
)

validation = TimeSeriesDataSet.from_dataset(training, val_data, predict=True, stop_randomization=True)
test = TimeSeriesDataSet.from_dataset(training, test_data, predict=True, stop_randomization=True)

# create dataloaders for model
batch_size = 64  # set this between 32 to 128
train_dataloader = training.to_dataloader(train=True, batch_size=batch_size, num_workers=11, persistent_workers=True)
val_dataloader = validation.to_dataloader(train=False, batch_size=batch_size, num_workers=11, persistent_workers=True)
test_dataloader = test.to_dataloader(train=False, batch_size=batch_size, num_workers=11, persistent_workers=True)