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Adaptive Sampling for Probabilistic Forecasting under Distribution Shift
Luca Masserano · Syama Sundar Rangapuram · Shubham Kapoor · Rajbir Nirwan · Youngsuk Park · Michael Bohlke-Schneider
Event URL: https://openreview.net/forum?id=oPHuNpJl3c »

The world is not static: This causes real-world time series to change over time because external, and potentially disruptive, events such as macroeconomic cycles or the COVID-19 pandemic change the underlying factors that influence the time series. Once such a data distribution shift happens, it will be part of the time series history and impact future forecasting attempts. We present an adaptive sampling strategy that selects the part of the history that is relevant for the recent data distribution. We achieve this by learning a discrete distribution over relevant time steps by Bayesian optimization. We instantiate this idea with a two-step, model-agnostic 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 reduces the forecasting error of the base model by 8.4%.

Author Information

Luca Masserano (Carnegie Mellon University)
Luca Masserano

I am a PhD student in the Department of Statistics at Carnegie Mellon University, where I work on Likelihood-Free Inference and Learning under Distribution Shift. I am advised by Ann B. Lee.

Syama Sundar Rangapuram (Amazon Research)
Shubham Kapoor (Amazon)
Rajbir Nirwan (Amazon Development Center Germany)
Youngsuk Park (Amazon, AWS AI Labs)
Michael Bohlke-Schneider (Amazon Development Center Germany)

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