面向缺失数据的实时网络流量预测：一种生成式模型方法
Real-Time Network Traffic Forecasting with Missing Data: A Generative Model Approach
AI总结：
针对实际场景中网络流量数据常存在缺失的问题，提出一种基于生成式模型的实时预测方法，将任务建模为张量补全问题，通过优化潜在表示实现快速预测，在Abilene数据集上100ms内完成预测且MAE低于0.002，并提供理论误差保证。
摘要：
实时网络流量预测对网络管理与提前资源分配至关重要。现有网络流量预测方法均基于网络流量数据可被完整观测的假设开展研究，但在实际场景中，受各类人为与自然因素影响，采集到的数据往往存在缺失。本文提出一种面向缺失数据的实时网络流量预测生成式模型方法。首先，将网络流量预测任务建模为张量补全问题；其次，引入预训练生成式模型以实现张量补全常见的低秩结构，该生成模型在预训练阶段可有效捕捉网络流量数据的内在低秩结构，实现从紧凑潜在表示到张量空间的映射；第三，无需直接优化高维张量，转而优化其潜在表示，简化了优化过程，可支撑实时预测。本文还建立了理论恢复保证，量化了所提方法的误差界。在真实数据集上的实验表明，经Abilene数据集验证，该方法可在100毫秒内完成准确的网络流量预测，平均绝对误差（MAE）低于0.002。
英文摘要：
Real-time network traffic forecasting is crucial for network management and early resource allocation. Existing network traffic forecasting approaches operate under the assumption that the network traffic data is fully observed. However, in practical scenarios, the collected data are often incomplete due to various human and natural factors. In this paper, we propose a generative model approach for real-time network traffic forecasting with missing data. Firstly, we model the network traffic forecasting task as a tensor completion problem. Secondly, we incorporate a pre-trained generative model to achieve the low-rank structure commonly associated with tensor completion. The generative model effectively captures the intrinsic low-rank structure of network traffic data during pre-training and enables the mapping from a compact latent representation to the tensor space. Thirdly, rather than directly optimizing the high-dimensional tensor, we optimize its latent representation, which simplifies the optimization process and enables real-time forecasting. We also establish a theoretical recovery guarantee that quantifies the error bound of the proposed approach. Experiments on real-world datasets demonstrate that our approach achieves accurate network traffic forecasting within 100 ms, with a mean absolute error (MAE) below 0.002, as validated on the Abilene dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
