Addressing Concept Shift in Online Time Series Forecasting: Detect-then-Adapt

arXiv:2403.14949v1 Announce Type: new
Abstract: Online updating of time series forecasting models aims to tackle the challenge of concept drifting by adjusting forecasting models based on streaming data. While numerous algorithms have been developed, most of them focus on model design and updating. In practice, many of these methods struggle with continuous performance regression in the face of accumulated concept drifts over time. To address this limitation, we present a novel approach, Concept \textbf{D}rift \textbf{D}etection an\textbf{D} \textbf{A}daptation (D3A), that first …

abstract adapt adjusting algorithms arxiv challenge concept cs.lg data design focus forecasting model design practice series shift streaming streaming data struggle them time series time series forecasting type