Shifting of Time Series data

[Elisa1234567890]

Hello,

i want to use FLAML to do a time series forecasting task. My dataset is structured as follows: timestamp, demand (output), several different exogenous features (input). One row in the dataset corresponds to the corresponding observations observed at the indicated timestamp. The exogenous features are past covariates that are only known up to the start of the forecast horizon (see Autogluon, past covariates).

As I predict the demand for a timestamp, the exogenous features are only available up to the previous timestamp. Since I am forced to include input variables in the FLAML.predict(), I am wondering whether i need to shift the time series manually beforehand such, that a row constists of the timestamp, the corresponding demand at this timestamp and the exogenous features shifted by the forecast horizon.

[Programmer-RD-AI]

1. Shifted Model: Exogenous feature are shifted, an advantage is that it provides inputs that directly align with forecast on the other hand it reduces the dataset size.
2. Unshifted Model: Here, we jus use the exogenous features as they are originally presented, it retains the full dataset but the features may not align with the forecasting which may make it hard to align features with the forecasting data.

The above is the simply aspect of Shifted Model's and Unshifted model's in terms of time series data, so let me know if there are any other questions :)