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Adaptive sampling for probabilistic forecasting under distribution shift

By Luca Masserano, Syama Rangapuram, Shubham Kapoor, Rajbir Singh Nirwan, Youngsuk Park, Michael Bohlke-Schneider
2022
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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.
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