  Time series anomaly detection is an important process for system monitoring
and model switching, among other applications in cyber-physical systems. In
this document, we present a fast subspace method for time series anomaly
detection, with a relatively low computational cost, that has been designed for
anomaly detection in real sensor signals corresponding to dynamical systems. We
also present some general results corresponding to the theoretical foundations
of our method, together with a prototypical algorithm to for time series
anomaly detection. Some numerical examples corresponding to applications of the
prototypical algorithm are presented, and some computational tools based on the
theory and algorithms presented in this paper, are provided.
