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}.

翻译：在线更新时间序列预测模型旨在通过基于流数据高效更新预测模型来解决概念漂移问题。许多算法专为在线时间序列预测设计，部分利用跨变量依赖性，另一些则假设变量间相互独立。针对在线时间序列建模中每种数据假设均有优劣的问题，我们提出**在线集成网络**（OneNet）。该方法动态更新并组合两个模型：一个专注于时间维度的依赖性建模，另一个专注于跨变量依赖性。我们的方法将基于强化学习的技术融入传统在线凸优化框架，实现两模型的线性组合及权重的动态调整。OneNet克服了经典在线学习方法在适应概念漂移时往往反应迟缓的主要缺陷。实验结果表明，与最先进方法相比，OneNet将在线预测误差降低了**超过50%**。代码已在\url{https://github.com/yfzhang114/OneNet}开源。