Tropical time series, iterated-sums signatures and quasisymmetric functions

Fuente: arXiv
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Main Authors: Diehl, Joscha, Ebrahimi-Fard, Kurusch, Tapia, Nikolas
Format: Preprint
Published: 2020
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author Diehl, Joscha
Ebrahimi-Fard, Kurusch
Tapia, Nikolas
author_facet Diehl, Joscha
Ebrahimi-Fard, Kurusch
Tapia, Nikolas
contents Aiming for a systematic feature-extraction from time series, we introduce the iterated-sums signature over arbitrary commutative semirings. The case of the tropical semiring is a central, and our motivating example. It leads to features of (real-valued) time series that are not easily available using existing signature-type objects. We demonstrate how the signature extracts chronological aspects of a time series, and that its calculation is possible in linear time. We identify quasisymmetric expressions over semirings as the appropriate framework for iterated-sums signatures over semiring-valued time series.
format Preprint
id arxiv_https___arxiv_org_abs_2009_08443
institution arXiv
publishDate 2020
record_format arxiv
spellingShingle Tropical time series, iterated-sums signatures and quasisymmetric functions
Diehl, Joscha
Ebrahimi-Fard, Kurusch
Tapia, Nikolas
Rings and Algebras
Computer Vision and Pattern Recognition
Machine Learning
60L10, 60L70, 16Y60, 93C55
Aiming for a systematic feature-extraction from time series, we introduce the iterated-sums signature over arbitrary commutative semirings. The case of the tropical semiring is a central, and our motivating example. It leads to features of (real-valued) time series that are not easily available using existing signature-type objects. We demonstrate how the signature extracts chronological aspects of a time series, and that its calculation is possible in linear time. We identify quasisymmetric expressions over semirings as the appropriate framework for iterated-sums signatures over semiring-valued time series.
title Tropical time series, iterated-sums signatures and quasisymmetric functions
topic Rings and Algebras
Computer Vision and Pattern Recognition
Machine Learning
60L10, 60L70, 16Y60, 93C55
url https://arxiv.org/abs/2009.08443