GPVAREstimator - AssertionError: about gluonts HOT 3 OPEN

```
variables = ['DBP', 'SBP', 'Resp', 'Temp', 'HR', 'O2Sat', 'MAP']
df_actual = pd.read_csv('merged_test.csv')
static_features = df_actual[['patient_id', 'Age', 'Gender', 'HospAdmTime']].drop_duplicates().reset_index(drop=True)
df_residuals = pd.DataFrame()

for variable in variables:
    # First, let's load forecasted values 
    df_forecast = pd.read_csv(f'forecasts_{variable}.csv')

    # Ensure that the data are ordered in the same way
    df_actual = df_actual.sort_values(by=['patient_id', 'ICULOS'])
    df_forecast = df_forecast.sort_values(by=['patient_id', 'ICULOS'])

    # Calculate residuals 
    residuals = df_actual[variable] - df_forecast[f'{variable}_forecast']

    # Add residual to df_residuals
    df_residuals[variable] = residuals
    df_residuals['patient_id'] = df_actual['patient_id']
    df_residuals['ICULOS'] = df_actual['ICULOS']

# Convert df_residuals to ListDataset
data_residuals = []
for patient_id, group in df_residuals.groupby('patient_id'):
    target = group[variables].values  # Use the residuals as target
    start = pd.Timestamp("1970-01-01 00:00") + pd.Timedelta(hours=group['ICULOS'].iloc[0])
    entry = {
        FieldName.TARGET: target,
        FieldName.START: start,  # Use the index as the start date
        FieldName.FEAT_STATIC_CAT: static_features[static_features['patient_id'] == patient_id][['Age', 'Gender']].values[0]
    }
    data_residuals.append(entry)

dataset_residuals = ListDataset(data_residuals, freq='1H', one_dim_target=False)
```