Characterizing Time Series Variability and Predictability from Information Geometry Dynamics

Shlomo Dubnov

Abstract: This paper presents a method for analyzing changes in information contents of time series based on a combined adaptive approximate similarity detection and temporal modeling using Bregman information. This work extends previous results on using information geometry for musical signals by suggesting a method for optimal model selection using Information Rate (IR) as a measure of an overall model predictability.