Hi everyone,

I have trained an NBeats model from PyTorch forecasting using [TimeSeriesDataSet — pytorch-forecasting documentation](https://Pythorch Forecasting)

using **TimeSeriesDataSet** method.

Here is the configuration of model and data:

```
training = TimeSeriesDataSet(
train_data,
time_idx="time",
target="target",
group_ids=["group"],
time_varying_unknown_reals=["target"],
max_encoder_length=100,
max_prediction_length=100
)
```

```
train_load= training.to_dataloader(train=True, batch_size=128)
test_load= testing.to_dataloader(train=False, batch_size=128)
```

**My goal is to look back the past data points and forecast the next 100 points in future**, and this is why I set the **max_prediction** = 100.

The test data has 234 samples.

I tried to look at the predicted value but the size of prediction is 35 and not 100 and this is super strange since I want to forecast the next 100 (the **prediction_horizon**).

The prediction is:

```
Pred = mymodel.predict(dataloaders=test_load)
len(Pred)
35
```

Also if you look at the

```
print(test_load.dataset)
TimeSeriesDataSet[length=35](...)
```

You see the output also shows the length is 35 and not 100.

Can someone please explain why I get 35 as the length and not 100: