用扩散模型预测延迟容忍网络的性能指标，更准且能量化不确定性。
Probabilistic QoS Metric Forecasting in Delay-Tolerant Networks Using Conditional Diffusion Models on Latent Dynamics
- 基于潜在时序动态的条件扩散模型，捕捉复杂非平稳数据
- 在多变量时间序列上显著优于主流概率预测方法
- 适合需要可靠预测的网络运维与路由决策场景
主动的QoS指标预测在延迟容忍网络（DTN）的维护与运营中广泛应用，可提升延迟、吞吐量、能耗和可靠性。该问题通常被建模为多变量时间序列预测，但传统均值回归方法难以充分捕捉数据复杂性，导致路由等实际任务性能下降。本文将DTN中的QoS指标预测转化为多变量时间序列上的概率预测问题，通过表征样本分布来量化预测不确定性。所提方法引入扩散模型，并将其与非平稳、多模式数据的潜在时序动态相结合。大量实验表明，该方法在多个基准上显著优于当前主流的概率时间序列预测方法。
原文摘要 · Abstract (English)
Active QoS metric prediction, commonly employed in the maintenance and operation of DTN, could enhance network performance regarding latency, throughput, energy consumption, and dependability. Naturally formulated as a multivariate time series forecasting problem, it attracts substantial research efforts. Traditional mean regression methods for time series forecasting cannot capture the data complexity adequately, resulting in deteriorated performance in operational tasks in DTNs such as routing. This paper formulates the prediction of QoS metrics in DTN as a probabilistic forecasting problem on multivariate time series, where one could quantify the uncertainty of forecasts by characterizing the distribution of these samples. The proposed approach hires diffusion models and incorporates the latent temporal dynamics of non-stationary and multi-mode data into them. Extensive experiments demonstrate the efficacy of the proposed approach by showing that it outperforms the popular probabilistic time series forecasting methods.
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。