Adaptive Sampling for Probabilistic Forecasting under Distribution Shift

TL;DR

This paper introduces an adaptive sampling method that dynamically selects relevant historical data for time series forecasting, effectively handling distribution shifts caused by external events and improving forecast accuracy.

Contribution

It proposes a novel Bayesian optimization-based adaptive sampling strategy that enhances forecasting models' ability to adapt to changing data distributions.

Findings

Reduces forecasting error in real-world datasets

Adapts effectively to distribution shifts

Improves accuracy over baseline models

Abstract

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.