对比12种深度模型，找出网络流量预测中又准又快的最佳方案。
Which Deep Learner? A Systematic Evaluation of Advanced Deep Forecasting Models Accuracy and Efficiency for Network Traffic Prediction
- 系统评估12种先进时序模型在多场景下的表现。
- 发现特定架构在准确率与效率间达到最佳平衡。
- 适合网络管理、运维优化等实际部署场景参考。
网络流量预测对自动化网络管理至关重要，属于复杂的时序预测问题。尽管深度学习模型能捕捉复杂模式，但不同网络环境和时间尺度下流量差异大，需明确有效的建模选择。本研究系统评估了12种先进时序预测模型（包括基于Transformer和传统DL方法），对比3个统计基线，在4个真实流量数据集上，覆盖多时间尺度与预测范围，全面评估性能、抗异常/数据缺失/外部干扰能力、数据效率及资源效率（时间、内存、能耗）。结果揭示性能分域、效率阈值，识别出在准确率与效率间平衡良好的模型架构，展现出对流量挑战的鲁棒性，并指明超越传统RNN的新方向。
原文摘要 · Abstract (English)
Network traffic prediction is essential for automating modern network management. It is a difficult time series forecasting (TSF) problem that has been addressed by Deep Learning (DL) models due to their ability to capture complex patterns. Advances in forecasting, from sophisticated transformer architectures to simple linear models, have improved performance across diverse prediction tasks. However, given the variability of network traffic across network environments and traffic series timescales, it is essential to identify effective deployment choices and modeling directions for network traffic prediction. This study systematically identify and evaluates twelve advanced TSF models -- including transformer-based and traditional DL approaches, each with unique advantages for network traffic prediction -- against three statistical baselines on four real traffic datasets, across multiple time scales and horizons, assessing performance, robustness to anomalies, data gaps, external factors, data efficiency, and resource efficiency in terms of time, memory, and energy. Results highlight performance regimes, efficiency thresholds, and promising architectures that balance accuracy and efficiency, demonstrating robustness to traffic challenges and suggesting new directions beyond traditional RNNs.
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