Adaptive Sampling for Probabilistic Forecasting under Distribution Shift

• URL: http://arxiv.org/abs/2302.11870v1
• Date: Thu, 23 Feb 2023 09:16:54 GMT
• Title: Adaptive Sampling for Probabilistic Forecasting under Distribution Shift
• Authors: Luca Masserano and Syama Sundar Rangapuram and Shubham Kapoor and Rajbir Singh Nirwan and Youngsuk Park and Michael Bohlke-Schneider
• Abstract summary: We present an adaptive sampling strategy that selects the part of the time series history that is relevant for forecasting. 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.
• Score: 9.769524837609174
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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