  We formulate a new inference task in the domain of multivariate time series
forecasting (MTSF), called Variable Subset Forecast (VSF), where only a small
subset of the variables is available during inference. Variables are absent
during inference because of long-term data loss (eg. sensor failures) or high
-> low-resource domain shift between train / test. To the best of our
knowledge, robustness of MTSF models in presence of such failures, has not been
studied in the literature. Through extensive evaluation, we first show that the
performance of state of the art methods degrade significantly in the VSF
setting. We propose a non-parametric, wrapper technique that can be applied on
top any existing forecast models. Through systematic experiments across 4
datasets and 5 forecast models, we show that our technique is able to recover
close to 95\% performance of the models even when only 15\% of the original
variables are present.

Machine Learning