'from gluonts.model.npts import NPTSEstimator
from gluonts.trainer import Trainer
from gluonts.model.npts import NPTSPredictor
Predictor1=NPTSPredictor(freq='H', prediction_length=24, context_length= 48, kernel_type='exponential',use_seasonal_model= True, use_default_time_features = True)
predictor=Predictor1.predict(dataset=train_ds,num_samples=100)
test_ds = ListDataset([{FieldName.TARGET: target, 
                        FieldName.START: start,
                        FieldName.FEAT_DYNAMIC_REAL: [fdr],
                       } 
                       for (target, start,fdr) in zip(target, 
                                                            start, 
                                                            feat_dynamic_real, 
                                                            )],
                     freq=freq)
from gluonts.evaluation.backtest import make_evaluation_predictions
forecast_it, ts_it = make_evaluation_predictions(
    dataset=test_ds,  # test dataset
    predictor=predictor,  # predictor
    num_samples=100,  # number of sample paths we want for evaluation
)