Dynamic Residual Calibration and Multi-Scale Fusion for Accurate Prediction of Non-Stationary Backend Indicators

Abstract

This paper proposes a residual calibration modeling and prediction method for non-stationary backend metrics to address the challenges of non-stationarity, dynamic dependency, and distribution drift in multi-dimensional metric sequences of cloud backend systems. The method employs a dual-layer collaborative architecture consisting of a primary predictor and a residual calibrator. The primary predictor captures the overall trend and temporal evolution of system metrics, while the residual calibrator performs structured modeling and adaptive correction of prediction errors to precisely compensate for sudden fluctuations and contextual variations. At the modeling level, a multi-scale feature fusion mechanism is introduced to enhance the model's sensitivity to variations across different temporal granularities, and consistency constraints are applied to ensure smoothness and stability at both global and local levels. To handle highly dynamic load scenarios in complex cloud environments, the model further establishes a closed-loop residual feedback pathway, enabling continuous learning and self-correction under distribution shifts. This effectively suppresses prediction bias and error accumulation. The proposed approach not only achieves a coordinated representation of global trends and local perturbations in structure but also integrates residual dynamic compensation and temporal consistency into a unified optimization framework. Experimental results on representative backend metric datasets demonstrate that the model achieves low-error and high-stability performance across multi-dimensional scenarios, significantly improving prediction accuracy and robustness under non-stationary distributions, and providing a scalable solution for dynamic modeling and intelligent operations of complex systems.