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Online updating of time series forecasting models aims to address the conceptdrifting problem by efficiently updating forecasting models based on streamingdata. Many algorithms are designed for online time series forecasting, withsome exploiting cross-variable dependency while others assume independenceamong variables. Given every data assumption has its own pros and cons inonline time series modeling, we propose \textbf{On}line \textbf{e}nsembling\textbf{Net}work (OneNet). It dynamically updates and combines two models, withone focusing on modeling the dependency across the time dimension and the otheron cross-variate dependency. Our method incorporates a reinforcementlearning-based approach into the traditional online convex programmingframework, allowing for the linear combination of the two models withdynamically adjusted weights. OneNet addresses the main shortcoming ofclassical online learning methods that tend to be slow in adapting to theconcept drift. Empirical results show that OneNet reduces online forecastingerror 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}.

OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling