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2020 | OriginalPaper | Chapter

A Fully Automated Periodicity Detection in Time Series

Authors : Tom Puech, Matthieu Boussard, Anthony D’Amato, Gaëtan Millerand

Published in: Advanced Analytics and Learning on Temporal Data

Publisher: Springer International Publishing

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Abstract

This paper presents a method to autonomously find periodicities in a signal. It is based on the same idea of using Fourier Transform and autocorrelation function presented in [12]. While showing interesting results this method does not perform well on noisy signals or signals with multiple periodicities. Thus, our method adds several new extra steps (hints clustering, filtering and detrending) to fix these issues. Experimental results show that the proposed method outperforms state of the art algorithms.
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Metadata
Title
A Fully Automated Periodicity Detection in Time Series
Authors
Tom Puech
Matthieu Boussard
Anthony D’Amato
Gaëtan Millerand
Copyright Year
2020
Book
Advanced Analytics and Learning on Temporal Data

Print ISBN: 978-3-030-39097-6

Electronic ISBN: 978-3-030-39098-3

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-39098-3_4

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