  The world is not static: This causes real-world time series to change over
time through external, and potentially disruptive, events such as macroeconomic
cycles or the COVID-19 pandemic. We present an adaptive sampling strategy that
selects the part of the time series history that is relevant for forecasting.
We achieve this by learning a discrete distribution over relevant time steps by
Bayesian optimization. We instantiate this idea with a two-step method that is
pre-trained with uniform sampling and then training a lightweight adaptive
architecture with adaptive sampling. We show with synthetic and real-world
experiments that this method adapts to distribution shift and significantly
reduces the forecasting error of the base model for three out of five datasets.
