This message was deleted.

Hi @Andrei Tulbure, You are right, using the

NeuralForecast.predict

method without additional arguments is analogous to forecast. The Automatic Forecasting methods are low level classes that operate purely with PyTorch and Numpy. For comparison of the

AutoTFT

,

AutoNBEATS

, and

AutoNHITS

we recommend you to use directly the

NeuralForecast

high-level wrapper class and its methods. Here is a

NeuralForecast

class documentation and usage example: • https://nixtla.github.io/neuralforecast/core.html • https://nixtla.github.io/neuralforecast/examples/longhorizon_with_nhits.html I opened this issue: https://github.com/Nixtla/neuralforecast/issues/394

Great! Thanks a lot! Another question, when do you think you would be able to also have an AutoNBeatsx ?

Hi @Andrei Tulbure, For the moment we don't have

AutoNBEATSx

, thanks for noticing it. We opened an issue for it. You can still recover an

AutoNBEATSx

-like behavior with the

AutoNHITS

model by setting

n_freq_downsample

to a list of ones. The

AutoNHITS

model already accepts, static, past and future exogenous data. Here is an example: https://github.com/Nixtla/neuralforecast/blob/main/nbs/examples/Exogenous_Variables.ipynb

Thanks!