Learning Stable Multivariate Baseline Models for Outbreak Detection
Sajid M. Siddiqi, Byron Boots, Geoffrey J. Gordon, Artur W. Dubrawski · 2007
We propose a novel technique for building generative models of real-valued multivariate time series data streams. Such models are of considerable utility as baseline simulators in anomaly detection systems. The proposed algorithm, based on Linear Dynamical Systems (LDS) [1], learns stable parameters efficiently while yielding more accurate results than previously known methods. The resulting model can be used to generate infinitely long sequences of realistic baselines using small samples of training data. BACKGROUND Time series analysis is an essential component of syndromic surveillance. Any outbreak detection technique, however, must rely on a stable and robust baseline model. Moreover, with the increasing utilization