[kfor]

They do mention covariates in section 6.1 - specifically how this method doesn’t support them but ideas on how they could in the future such as via stacking:

> In this work, we have focused on univariate time series forecasting since it constitutes the most common of real-world time series use-cases. Nevertheless, practical forecasting tasks often involve additional information that must be taken into account. One example involves covariates, that can be either time-independent (e.g., color of the product) or time-varying (e.g., on which days the product is on sale). Another closely related problem is multivariate forecasting, where historic values of one time series (e.g., interest rates) can influence the forecast for another time series (e.g., housing prices). The number of covariates or multivariate dimensions can vary greatly across tasks, which makes it challenging to train a single model that can handle all possible combinations. A possible solution may involve training task-specific adaptors that inject the covariates into the pretrained forecasting model (Rahman et al., 2020). As another option, we can build stacking ensembles (Ting & Witten, 1997) of Chronos and other light-weight models that excel at handling covariates such as LightGBM (Ke et al., 2017).

[sampo]

Ah. Thank you. The same concept goes under different names, so one needs to search for all of "exogenous variables", "external regressors", "external factors" and "covariates".