Predicting the behavior of real-time traffic (e.g., VoIP) in mobility scenarios could help the operators to better plan their network infrastructures and to optimize the allocation of resources. Accordingly, in this work the authors propose a forecasting analysis of crucial QoS/QoE descriptors (some of which neglected in the technical literature) of VoIP traffic in a real mobile environment. The problem is formulated in terms of a multivariate time series analysis. Such a formalization allows to discover and model the temporal relationships among various descriptors and to forecast their behaviors for future periods. Techniques such as Vector Autoregressive models and machine learning (deep-based and tree-based) approaches are employed and compared in terms of performance and time complexity, by reframing the multivariate time series problem into a supervised learning one. Moreover, a series of auxiliary analyses (stationarity, orthogonal impulse responses, etc.) are performed to discover the analytical structure of the time series and to provide deep insights about their relationships. The whole theoretical analysis has an experimental counterpart since a set of trials across a real-world LTE-Advanced environment has been performed to collect, post-process and analyze about 600,000 voice packets, organized per flow and differentiated per codec.

翻译：预测移动场景中实时流量（如VoIP）的行为有助于运营商优化网络基础设施规划与资源配置。基于此，本文作者针对真实移动环境中VoIP流量的关键QoS/QoE描述符（部分在技术文献中被忽略）提出预测分析。该问题被建模为多元时间序列分析，这一形式化方法能够发现并刻画各描述符之间的时序关系，进而预测其未来行为。通过将多元时间序列问题重构为监督学习问题，本文采用向量自回归模型与机器学习方法（深度学习和基于树的方法）进行性能与时间复杂度的对比。此外，还开展了一系列辅助分析（如平稳性检验、正交脉冲响应等）以揭示时间序列的分析结构及其深层关联。全部理论分析均通过实验验证：在真实LTE-Advanced环境中开展多组实验，采集、后处理并分析了约60万个语音数据包（按流组织并按编解码器分类）。