Adaptive Sampling for Probabilistic Forecasting under Distribution Shift

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.

A progressive mesh simplification algorithm based on neural implicit representation

“…[19][20][21][22][23][24][25][26][27][28][29] At the same time, with the development of machine learning, probabilistic models have been widely used in computer vision and other fields. [30][31][32][33][34][35][36][37][38][39][40] The algorithm in this article aims to propose a new PM framework by using NePMs and probabilistic models.…”