Wavelet space partitioning for symbolic time series analysis

A crucial step in symbolic time series analysis (STSA) of observed data is symbol sequence generation that relies on partitioning the phase-space of the underlying dynamical system. We present a novel partitioning method, called wavelet-space (WS) partitioning, as an alternative to symbolic false nearest neighbour (SFNN) partitioning. While the WS and SFNN partitioning methods have been demonstrated to yield comparable performance for anomaly detection on laboratory apparatuses, computation of WS partitioning is several orders of magnitude faster than that of the SFNN partitioning.