`run_test_nbeatsx` confusion

[cchallu]

`run_test_nbeatsx` confusion

[cchallu]

Hi @MC-Dave. Yes, we assume the exogenous variables are known for the forecasting window. In EPF, the exogenous variables correspond to predictions of demand and offer for the forecasting window.

We have a general implementation of the model in our NeuralForecast library (https://github.com/Nixtla/neuralforecast). This implementation allows for 3 types of exogenous variables: static, future temporal (available in the forecasting window), and historic temporal (unavailable for future values). This tutorial shows how to use a model with different types of variables: https://nixtla.github.io/neuralforecast/examples/exogenous_variables.html

[MC-Dave]

@cchallu Thank you very much for the quick reply.

I assume there is no support for historic temporal variables in this repo?

I suppose I misunderstood the include_var_dict and the meaning of the offsets.

From the code it is implied that variables like week_day are known ahead of time, which is why you can set it to -1 for future. The Other variables, including y, must be -2 or less. -2 here meaning past variable.

This comment is under def run_val_nbeatsx

# This dictionary will be used to select particular lags as inputs for each y and exogenous variables.
# For eg, -1 will include the future (corresponding to the forecasts variables), -2 will add the last
# available day (1 day lag), etc.

I have used the NeuralForecast library prior to working with this project. I tried this project because it gives a much greater control over the parameters available, as well as implemented a very helpful hyperparameter optimization loop.

Thank you again for your assistance