Deep Learning-Based Uncertainty-Driven Robust Time Series Forecasting for Backend Service Metrics

This study addresses the challenge of forecasting backend service metrics in highly dynamic and disturbed environments and proposes a robust time series prediction framework based on uncertainty estimation. The method first applies a multi-scale encoding structure to decompose and represent service metric sequences, allowing the model to capture mixed characteristics of trends, periodic patterns, and noise disturbances. It then constructs a cross-sequence dependency modeling module to describe structural coupling among multiple metrics so that the model can identify implicit interaction patterns across different service nodes in backend systems. On this basis, the framework outputs both the mean and variance of future metrics through distributed prediction heads, enabling joint characterization of predictions and their associated risk ranges. A noise response mechanism is incorporated to enhance model stability under anomalous disturbances. To improve the controllability of the overall prediction process, a risk adjustment module is designed to transform uncertainty signals into actionable adjustment factors, allowing the model to maintain consistent outputs in high volatility environments. The experiments evaluate hyperparameter sensitivity, environmental sensitivity, and data sensitivity. The results show that the proposed framework maintains high stability and robustness under multiple source disturbances and effectively suppresses error propagation caused by anomalous samples, structural changes, and system scaling. By jointly modeling point predictions and risk estimation, the study verifies the effectiveness of an uncertainty-driven robust forecasting framework for backend metric management and system reliability, providing a scalable technical path for data-driven analysis in complex systems.