Year: 2026
Type: article
Abstract: Time series forecasting is integral to diverse fields, significantly influencing both industrial production and human activities. In the real world, time series data often exists in the form of streaming data and its distribution often changes over time, which is referred to as concept drift. This results in a gradual decline in the performance of traditional deep learning models as time progresses. Current online learning methods attempt to alleviate this issue by employing online adaptation methods in temporal dimension. However, they overlook the changes and drifts in the inter-variable association within time series. To this end, we propose a novel Online Dynamic Graph Network (ODGNet). ODGNet represents variable associations as a matrix polynomial and acquires polynomial coefficients based on online gradients, which models the evolutionary trends of spatial patterns. Furthermore, we emphasize the lossless information mapping between the adjacency matrix and its corresponding polynomial coefficient. Based on this mapping, a graph memory module with low memory consumption is proposed to avoid catastrophic forgetting when graph drift occurs. And a graph drift awareness mechanism is designed to rapidly detect graph drift. Experimental results demonstrate that ODGNet achieves a significant reduction in forecasting error compared to the existing online learning methods on eight benchmark datasets. The code is available at https://github.com/LiuYasuo/ODGNet.
Authors: Yushuo Liu, Yulong Wang, Kai Wang
Institution: Nankai University
Language: English
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,645
OpenAlex ID: W7147323780