Enhancing Time Series Forecasting Models under Concept Drift by Data-centric Online Ensembling

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 proposed recently, 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{D}ata-centric \textbf{On}line \textbf{e}nsembling \textbf{Net}work (\abbr), which allows for the linear combination of the two models with dynamically adjusted weights based on the data bias. Empirical results show that \abbr reduces online forecasting error by more than $\mathbf{50\%}$ compared to the State-Of-The-Art (SOTA) method.