对比深度学习模型在真实网络流量预测中的表现
Comparative Analysis of Deep Learning Models for Real-World ISP Network Traffic Forecasting
- 使用40周多变量时间序列数据，评估多种深度学习模型
- 发现不同网络粒度下精度与效率的权衡关系
- 提供可复现方法与基准，便于后续研究对比
精准的网络流量预测对互联网服务提供商（ISP）优化资源、提升用户体验和防范异常至关重要。本研究基于近期发布的、来自CESNET3 ISP网络的综合性真实世界网络流量数据集CESNET-TimeSeries24，对当前先进的深度学习模型进行了评估。该数据集涵盖40周的多变量时间序列。研究结果揭示了在不同网络粒度下，预测精度与计算效率之间的平衡关系。此外，本文建立了一套可复现的方法论，支持现有方法的直接比较，深入分析其优缺点，并为未来使用该数据集的研究提供了基准。
原文摘要 · Abstract (English)
Accurate network traffic forecasting is essential for Internet Service Providers (ISP) to optimize resources, enhance user experience, and mitigate anomalies. This study evaluates state-of-the-art deep learning models on CESNET-TimeSeries24, a recently published, comprehensive real-world network traffic dataset from the ISP network CESNET3 spanning multivariate time series over 40 weeks. Our findings highlight the balance between prediction accuracy and computational efficiency across different levels of network granularity. Additionally, this work establishes a reproducible methodology that facilitates direct comparison of existing approaches, explores their strengths and weaknesses, and provides a benchmark for future studies using this dataset.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。