DMCnet: Multi-Scale Trend and Patch Coordination for Online Time Series Forecasting under Concept Drift

Online time series forecasting must adapt to evolving data distributions while limiting the propagation of errors across sequential updates. This paper presents DMCnet, a multi-scale coordination framework for online multivariate time series forecasting under concept drift. DMCnet employs two complementary predictors: a trend-oriented branch that extracts multi-scale temporal patterns using smoothed moving-average representations and dilated temporal convolutions, and a patch-oriented branch that models local dynamics from segmented input patches. The forecasts are combined online through convex weight integration, allowing the model to adjust the relative contributions of long-term and local predictors as the stream evolves. To reduce the effect of unstable short-term updates, we further introduce the Online Noise Sensitivity Protector (ONSP), which uses the discrepancy between the incoming sequence and its smoothed trend as a data-dependent signal to regulate short-term weight updates. The resulting mechanism is designed as a practical stabilization strategy rather than as a direct statistical estimator of observation noise. We evaluate DMCnet on five real-world datasets, including the newly released PCpower dataset, which records personal-computer power consumption under diverse usage scenarios. Across the evaluated datasets and forecasting settings, DMCnet reduces the average cumulative mean squared error (MSE) and mean absolute error (MAE) by 24.67% and 19.32%, respectively, compared with the strongest competing methods. These results indicate that coordinating multi-scale trend representations with local patch representations can improve online forecasting robustness under distributional change.