Twisty Takens: A Geometric Characterization of Good Observations on Dense Trajectories
Boyan Xu, Christopher J. Tralie, Alice Antia, Michael Lin, Jose A., Perea

TL;DR
This paper provides a geometric framework for selecting observation functions in nonlinear time series analysis to ensure successful reconstruction of underlying dynamics, demonstrated through diverse examples and numerical experiments.
Contribution
It establishes conditions on observation functions on manifolds that guarantee accurate embeddings, expanding the class of reconstructible dynamical systems.
Findings
Constructed time series tracing various topological shapes.
Applied persistent cohomology to recover low-dimensional dynamics.
Demonstrated the effectiveness of Eilenberg-MacLane coordinates in analysis.
Abstract
In nonlinear time series analysis and dynamical systems theory, Takens' embedding theorem states that the sliding window embedding of a generic observation along trajectories in a state space, recovers the region traversed by the dynamics. This can be used, for instance, to show that sliding window embeddings of periodic signals recover topological loops, and that sliding window embeddings of quasiperiodic signals recover high-dimensional torii. However, in spite of these motivating examples, Takens' theorem does not in general prescribe how to choose such an observation function given particular dynamics in a state space. In this work, we state conditions on observation functions defined on compact Riemannian manifolds, that lead to successful reconstructions for particular dynamics. We apply our theory and construct families of time series whose sliding window embeddings trace tori,…
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Taxonomy
TopicsTopological and Geometric Data Analysis · Advanced Neuroimaging Techniques and Applications · Mathematical Dynamics and Fractals
