OneNet: Enhancing Time Series Forecasting Models under Concept Drift by
  Online Ensembling
Yi-Fan Zhang Qingsong Wen Xue Wang Weiqi Chen Liang Sun Zhang Zhang Liang Wang Rong Jin Tieniu Tan
Online updating of time series forecasting models aims to address the concept
drifting problem by efficiently updating forecasting models based on streaming
data. Many algorithms are designed for online time series forecasting, with
some exploiting cross-variable dependency while others assume independence
among variables. Given every data assumption has its own pros and cons in
online time series modeling, we propose \textbf{On}line \textbf{e}nsembling
\textbf{Net}work (OneNet). It dynamically updates and combines two models, with
one focusing on modeling the dependency across the time dimension and the other
on cross-variate dependency. Our method incorporates a reinforcement
learning-based approach into the traditional online convex programming
framework, allowing for the linear combination of the two models with
dynamically adjusted weights. OneNet addresses the main shortcoming of
classical online learning methods that tend to be slow in adapting to the
concept drift. Empirical results show that OneNet reduces online forecasting
error by more than $\mathbf{50\%}$ compared to the State-Of-The-Art (SOTA)
method. The code is available at \url{https://github.com/yfzhang114/OneNet}.