Information Causality in the Prediction of Stochastic Networks

  • Numerous real world systems of major interest are modeled as sets of analog continuous stochastic processes with delayed and varying causal relationships. Yet, studying their dynamics becomes often difficult, as it involves sensing, understanding and predicting a system of inter-dependent random variables in a given context and over time. In the present work we develop a systematic, rigorous and efficient framework to structurally characterize and forecast the future states of such systems in a scalable manner. In particular we use an improved maximum spanning tree method to capture the causal dependence structure based on directed information theory. To this end we address the sparsity problem in information causality estimation in general, and directed information in particular and propose a new method that identifies and eliminates redundant calculations. To predict the behavior of child nodes based on their inferred causal parents we use a linear model aiming to capture the closest approximation of functional relations. Further dependencies are accounted for using causal conditional information and potential links that improve child nodes estimation are added. The result is a comprehensive and scalable approach to understanding and predicting large sets of inter-dependent narrowband processes, as we demonstrate on several synthetic datasets.

Download full text

Cite this publication

  • Export Bibtex
  • Export RIS

Citable URL (?):

Search for this publication

Search Google Scholar Search Catalog of German National Library Search OCLC WorldCat Search Bielefeld Academic Search Engine
Meta data
Publishing Institution:IRC-Library, Information Resource Center der Jacobs University Bremen
Granting Institution:Jacobs Univ.
Author:Simona Maria Cabuz
Referee:Giuseppe Thadeu Freitas de Abreu, Mathias Bode, Koji Ishibashi
Advisor:Giuseppe Thadeu Freitas de Abreu
Persistent Identifier (URN):urn:nbn:de:gbv:579-opus-1006053
Document Type:PhD Thesis
Language:English
Date of Successful Oral Defense:2016/09/08
Date of First Publication:2016/11/21
Academic Department:Computer Science & Electrical Engineering
PhD Degree:Electrical Engineering
Focus Area:Mobility
Other Countries Involved:Japan
Library of Congress Classification:T Technology / TA Engineering (General). Civil engineering (General) / TA329-348 Engineering mathematics. Engineering analysis
Call No:Thesis 2016/41

$Rev: 13581 $