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Journal article 1179 views

Cyclicality, Periodicity and the Topology of Time Series

Pawel Dlotko Orcid Logo, Wanling Rudkin, Simon Rudkin Orcid Logo

arXiv

Swansea University Authors: Pawel Dlotko Orcid Logo, Wanling Rudkin, Simon Rudkin Orcid Logo

Abstract

Periodic and semi periodic patterns are very common in nature. In this paper we introduce a topological toolbox aiming in detecting and quantifying periodicity. The presented technique is of a general nature and may be employed wherever there is suspected cyclic behaviour in a time series with no tr...

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Published in: arXiv
Published:
Online Access: https://arxiv.org/abs/1905.12118v1
URI: https://cronfa.swan.ac.uk/Record/cronfa51890
first_indexed 2019-09-12T20:48:46Z
last_indexed 2024-11-14T12:02:20Z
id cronfa51890
recordtype SURis
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spelling 2023-05-22T14:13:23.8641157 v2 51890 2019-09-12 Cyclicality, Periodicity and the Topology of Time Series 403ec9c6f5967333948eabebe06a75f5 0000-0001-5352-3102 Pawel Dlotko Pawel Dlotko true false dcbad51452ff28fbfdf7e82d4669c6a4 Wanling Rudkin Wanling Rudkin true false 93f12293ea6ed07ae8162cf25659c5f2 0000-0001-8622-7318 Simon Rudkin Simon Rudkin true false 2019-09-12 MACS Periodic and semi periodic patterns are very common in nature. In this paper we introduce a topological toolbox aiming in detecting and quantifying periodicity. The presented technique is of a general nature and may be employed wherever there is suspected cyclic behaviour in a time series with no trend. The approach is tested on a number of real-world examples enabling us to consistently demonstrate an ability to recognise periodic behaviour where conventional techniques fail to do so. Quicker to react to changes in time series behaviour, and with a high robustness to noise, the toolbox offers a powerful way to deeper understanding of time series dynamics. Journal Article arXiv Data Topology, Cyclicality, Time Series, Periodicity Detection 0 0 0 0001-01-01 https://arxiv.org/abs/1905.12118v1 Preprint article before certification by peer review COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University 2023-05-22T14:13:23.8641157 2019-09-12T17:08:44.4901315 Faculty of Humanities and Social Sciences Pawel Dlotko 0000-0001-5352-3102 1 Wanling Rudkin 2 Simon Rudkin 0000-0001-8622-7318 3
title Cyclicality, Periodicity and the Topology of Time Series
spellingShingle Cyclicality, Periodicity and the Topology of Time Series
Pawel Dlotko
Wanling Rudkin
Simon Rudkin
title_short Cyclicality, Periodicity and the Topology of Time Series
title_full Cyclicality, Periodicity and the Topology of Time Series
title_fullStr Cyclicality, Periodicity and the Topology of Time Series
title_full_unstemmed Cyclicality, Periodicity and the Topology of Time Series
title_sort Cyclicality, Periodicity and the Topology of Time Series
author_id_str_mv 403ec9c6f5967333948eabebe06a75f5
dcbad51452ff28fbfdf7e82d4669c6a4
93f12293ea6ed07ae8162cf25659c5f2
author_id_fullname_str_mv 403ec9c6f5967333948eabebe06a75f5_***_Pawel Dlotko
dcbad51452ff28fbfdf7e82d4669c6a4_***_Wanling Rudkin
93f12293ea6ed07ae8162cf25659c5f2_***_Simon Rudkin
author Pawel Dlotko
Wanling Rudkin
Simon Rudkin
author2 Pawel Dlotko
Wanling Rudkin
Simon Rudkin
format Journal article
container_title arXiv
institution Swansea University
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
url https://arxiv.org/abs/1905.12118v1
document_store_str 0
active_str 0
description Periodic and semi periodic patterns are very common in nature. In this paper we introduce a topological toolbox aiming in detecting and quantifying periodicity. The presented technique is of a general nature and may be employed wherever there is suspected cyclic behaviour in a time series with no trend. The approach is tested on a number of real-world examples enabling us to consistently demonstrate an ability to recognise periodic behaviour where conventional techniques fail to do so. Quicker to react to changes in time series behaviour, and with a high robustness to noise, the toolbox offers a powerful way to deeper understanding of time series dynamics.
published_date 0001-01-01T07:48:12Z
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score 11.099074

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