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Iterated Integrals and Population Time Series Analysis

Conference paper
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Part of the Abel Symposia book series (ABEL, volume 15)

Abstract

One of the core advantages topological methods for data analysis provide is that the language of (co)chains can be mapped onto the semantics of the data, providing a natural avenue for human understanding of the results. Here, we describe such a semantic structure on Chen’s classical iterated integral cochain model for paths in Euclidean space. Specifically, in the context of population time series data, we observe that iterated integrals provide a model-free measure of pairwise influence that can be used for causality inference. Along the way, we survey recent results and applications, review the current standard methods for causality inference, and briefly provide our outlook on generalizations to go beyond time series data.

Notes

Acknowledgements

D.L. is supported by the Office of the Assistant Secretary of Defense Research & Engineering through ONR N00014-16-1-2010, and the Natural Sciences and Engineering Research Council of Canada (NSERC) PGS-D3.

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Authors and Affiliations

  1. 1.Department of Mathematical SciencesUniversity of DelawareNewarkUSA
  2. 2.Department of MathematicsUniversity of PennsylvaniaPhiladelphiaUSA

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