Multivariate Time Series characterization and forecasting of VoIP
  traffic in real mobile networks
Mario Di Mauro Giovanni Galatro Fabio Postiglione Wei Song Antonio Liotta
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.