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.

翻译：网络流量预测对于实现现代网络管理自动化至关重要。这是一个具有挑战性的时间序列预测问题，深度学习模型因其捕捉复杂模式的能力而被广泛采用。从复杂的Transformer架构到简单的线性模型，预测技术的进步已提升了各类预测任务的性能。然而，鉴于网络流量在不同网络环境和时间尺度上的高度可变性，确定有效的部署选择和建模方向对于网络流量预测至关重要。本研究系统性地识别并评估了十二种先进的时间序列预测模型——包括基于Transformer的传统深度学习方法，每种模型在网络流量预测中均具有独特优势——在四个真实流量数据集上，针对三个统计基线模型，跨越多个时间尺度和预测范围，综合评估了模型性能、对异常值/数据缺失/外部因素的鲁棒性、数据效率，以及在时间、内存和能耗方面的资源效率。研究结果揭示了不同性能区间、效率阈值，以及能够平衡准确性与效率的潜力架构，证明了模型对流量挑战的鲁棒性，并为超越传统RNN的新研究方向提供了启示。