Monitoring Time-Varying Network Streams Using State-Space Models

In this paper, we develop an online monitoring methodology for the time-varying cyclical streams of network data, that combines a baseline state-space model and statistical control schemes to monitor departures from the baseline model. Parameters of the state space models are initialized based on the training data, and updated for each incoming observation. The statistical control schemes for monitoring are designed based on forecasting errors from the baseline model, under the framework of statistical change detection. We demonstrate the effectiveness of our methodology using measurement data from an EVDO wireless network.