The insertion method to invert the signature of a path
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Abstract (arXiv)
The signature is a representation of a path as an infinite sequence of its iterated integrals. Under certain assumptions, the signature characterizes the path, up to translation and reparameterization. Therefore, a crucial question of interest is the development of efficient algorithms to invert the signature, i.e., to reconstruct the path from the information of its (truncated) signature. In this article, we study the insertion procedure, originally introduced by Chang and Lyons (2019), from both a theoretical and a practical point of view. After describing our version of the method, we give its rate of convergence for piecewise linear paths, accompanied by an implementation in Pytorch. The algorithm is parallelized, meaning that it is very efficient at inverting a batch of signatures simultaneously. Its performance is illustrated with both real-world and simulated examples.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Apr 2023.
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Citing papers per year: 2024: 2; 2025: 0; 2026: 1.
3 papers.
- 2024SigDiffusions: Score-Based Diffusion Models for Time Series via Log-Signature Embeddingscited by 4
- 2024Path Signatures and Graph Neural Networks for Slow Earthquake Analysis: Better Together?cited by 0
- 2026Probabilistic Signature Inversion: Learning Conditional Distributions from Truncated Signaturescited by 0
- 2026Probabilistic Signature Inversion: Learning Conditional Distributions from Truncated Signatures13 Jun 2026
- 2024SigDiffusions: Score-Based Diffusion Models for Time Series via Log-Signature Embeddings14 Jun 2024
- 2024Path Signatures and Graph Neural Networks for Slow Earthquake Analysis: Better Together?5 Feb 2024