Accurate network-traffic forecasting enables proactive capacity planning and anomaly detection in Internet Service Provider (ISP) networks. Recent advances in time-series foundation models (TSFMs) have demonstrated strong zero-shot and few-shot generalization across diverse domains, yet their effectiveness for computer networking remains unexplored. This paper presents a systematic evaluation of a TSFM, IBM's Tiny Time Mixer (TTM), on the CESNET-TimeSeries24 dataset, a 40-week real-world ISP telemetry corpus. We assess TTM under zero-shot and few-shot settings across multiple forecasting horizons (hours to days), aggregation hierarchies (institutions, subnets, IPs), and temporal resolutions (10-minute and hourly). Results show that TTM achieves consistent accuracy (RMSE 0.026-0.057) and stable $R^2$ scores across horizons and context lengths, outperforming or matching fully trained deep learning baselines such as GRU and LSTM. Inference latency remains under 0.05s per 100 points on a single MacBook Pro using CPU-only computation, confirming deployability without dedicated GPU or MPS acceleration. These findings highlight the potential of pretrained TSFMs to enable scalable, efficient, and training-free forecasting for modern network monitoring and management systems.

翻译：准确的网络流量预测有助于互联网服务提供商（ISP）网络实现主动容量规划与异常检测。时间序列基础模型（TSFMs）的最新进展已在多个领域展现出强大的零样本与少样本泛化能力，但其在计算机网络领域的有效性尚未得到验证。本文基于CESNET-TimeSeries24数据集（一个包含40周真实世界ISP遥测数据的语料库），对IBM的Tiny Time Mixer（TTM）时间序列基础模型进行了系统性评估。我们在多种预测范围（数小时至数天）、聚合层级（机构、子网、IP地址）及时间分辨率（10分钟与小时级）下，测试了TTM在零样本与少样本场景下的性能。结果表明，TTM在不同预测范围与上下文长度下均能保持稳定的准确度（RMSE 0.026-0.057）与$R^2$分数，其表现优于或匹配完全训练的深度学习基线模型（如GRU与LSTM）。在仅使用CPU计算的单台MacBook Pro上，每100个数据点的推理延迟始终低于0.05秒，证实了该模型无需专用GPU或MPS加速即可部署的可行性。这些发现凸显了预训练时间序列基础模型为现代网络监控与管理系统提供可扩展、高效且无需训练即可使用的预测能力的潜力。