Information directionality in coupled time series using transcripts

TL;DR

This paper introduces a novel framework using transcripts from ordinal symbolic dynamics to assess coupling directionality in time series, providing lower-dimensional measures that unify existing quantifiers.

Contribution

It develops a mathematical structure based on permutation groups to derive new information flow measures from transcripts, generalizing and unifying existing directionality metrics.

Findings

New transcript-based measures estimate information flow in lower dimensions.

Measures reduce to established quantifiers under certain conditions.

Framework encompasses various existing directionality measures.

Abstract

In ordinal symbolic dynamics, transcripts describe the algebraic relationship between ordinal patterns. Using the concept of transcript, we exploit the mathematical structure of the group of permutations to derive properties and relations among information measures of the symbolic representations of time series. These theoretical results are then applied for the assessment of coupling directionality in dynamical systems, where suitable coupling directionality measures are introduced depending only on transcripts. These novel measures estimate information flow in lower space dimension and reduce to well-established coupling directionality quantifiers when some general conditions are satisfied. Furthermore, by generalizing the definition of transcript to ordinal patterns of different lengths, several of the commonly used information directionality measures can be encompassed within the same framework.