Time Series Forecasting for Network Traffic
Changjiang Sun, Xuting Chen, Jiachao Si, Xinmin Liu · 2025
With the rapid development of intelligent networking technologies, time series forecasting models have become vital for network traffic management, enabling performance optimization and resource scheduling in large-scale network systems. This study proposes an adaptive multi-scale sequence kernel fusion model tailored for network traffic prediction tasks. By leveraging interface-level time series data from network devices, the model captures complex temporal patterns and cross-metric dependencies, addressing the limitations of traditional models that treat metrics independently. The model consists of three core modules: a Multi-scale Dynamic Modeling module, an Aggregation and Redistribution module, and a Multi-Expert Fusion module. Together, they enhance the model’s ability to represent periodicity, non-stationarity, and structural dependencies in heterogeneous traffic data. Experimental results demonstrate improved accuracy and robustness across diverse network environments.