Measuring the Topological Time Irreversibility of Time Series With the Degree-Vector-Based Visibility Graph Method

Time irreversibility of a time series, which can be defined as the variance of properties under the time-reversal transformation, is a cardinal property of non-equilibrium systems and is associated with predictability in the study of financial time series. Recent pieces of literature have proposed t...

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Autores principales: Ryutaro Mori, Ruiyun Liu, Yu Chen
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Lenguaje:EN
Publicado: Frontiers Media S.A. 2021
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Acceso en línea:https://doaj.org/article/58bfc738c58b4abb952ebeaee66e1dc2
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spelling oai:doaj.org-article:58bfc738c58b4abb952ebeaee66e1dc22021-11-12T06:26:16ZMeasuring the Topological Time Irreversibility of Time Series With the Degree-Vector-Based Visibility Graph Method2296-424X10.3389/fphy.2021.777958https://doaj.org/article/58bfc738c58b4abb952ebeaee66e1dc22021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fphy.2021.777958/fullhttps://doaj.org/toc/2296-424XTime irreversibility of a time series, which can be defined as the variance of properties under the time-reversal transformation, is a cardinal property of non-equilibrium systems and is associated with predictability in the study of financial time series. Recent pieces of literature have proposed the visibility-graph-based approaches that specifically refer to topological properties of the network mapped from a time series, with which one can quantify different degrees of time irreversibility within the sets of statistically time-asymmetric series. However, all these studies have inadequacies in capturing the time irreversibility of some important classes of time series. Here, we extend the visibility-graph-based method by introducing a degree vector associated with network nodes to represent the characteristic patterns of the index motion. The newly proposed method is parameter-free and temporally local. The validation to canonical synthetic time series, in the aspect of time (ir)reversibility, illustrates that our method can differentiate a non-Markovian additive random walk from an unbiased Markovian walk, as well as a GARCH time series from an unbiased multiplicative random walk. We further apply the method to the real-world financial time series and find that the price motions occasionally equip much higher time irreversibility than the calibrated GARCH model does.Ryutaro MoriRuiyun LiuYu ChenFrontiers Media S.A.articletime series analysistime-reversibilityvisibility graphtime series motifstime series similarityPhysicsQC1-999ENFrontiers in Physics, Vol 9 (2021)
institution DOAJ
collection DOAJ
language EN
topic time series analysis
time-reversibility
visibility graph
time series motifs
time series similarity
Physics
QC1-999
spellingShingle time series analysis
time-reversibility
visibility graph
time series motifs
time series similarity
Physics
QC1-999
Ryutaro Mori
Ruiyun Liu
Yu Chen
Measuring the Topological Time Irreversibility of Time Series With the Degree-Vector-Based Visibility Graph Method
description Time irreversibility of a time series, which can be defined as the variance of properties under the time-reversal transformation, is a cardinal property of non-equilibrium systems and is associated with predictability in the study of financial time series. Recent pieces of literature have proposed the visibility-graph-based approaches that specifically refer to topological properties of the network mapped from a time series, with which one can quantify different degrees of time irreversibility within the sets of statistically time-asymmetric series. However, all these studies have inadequacies in capturing the time irreversibility of some important classes of time series. Here, we extend the visibility-graph-based method by introducing a degree vector associated with network nodes to represent the characteristic patterns of the index motion. The newly proposed method is parameter-free and temporally local. The validation to canonical synthetic time series, in the aspect of time (ir)reversibility, illustrates that our method can differentiate a non-Markovian additive random walk from an unbiased Markovian walk, as well as a GARCH time series from an unbiased multiplicative random walk. We further apply the method to the real-world financial time series and find that the price motions occasionally equip much higher time irreversibility than the calibrated GARCH model does.
format article
author Ryutaro Mori
Ruiyun Liu
Yu Chen
author_facet Ryutaro Mori
Ruiyun Liu
Yu Chen
author_sort Ryutaro Mori
title Measuring the Topological Time Irreversibility of Time Series With the Degree-Vector-Based Visibility Graph Method
title_short Measuring the Topological Time Irreversibility of Time Series With the Degree-Vector-Based Visibility Graph Method
title_full Measuring the Topological Time Irreversibility of Time Series With the Degree-Vector-Based Visibility Graph Method
title_fullStr Measuring the Topological Time Irreversibility of Time Series With the Degree-Vector-Based Visibility Graph Method
title_full_unstemmed Measuring the Topological Time Irreversibility of Time Series With the Degree-Vector-Based Visibility Graph Method
title_sort measuring the topological time irreversibility of time series with the degree-vector-based visibility graph method
publisher Frontiers Media S.A.
publishDate 2021
url https://doaj.org/article/58bfc738c58b4abb952ebeaee66e1dc2
work_keys_str_mv AT ryutaromori measuringthetopologicaltimeirreversibilityoftimeserieswiththedegreevectorbasedvisibilitygraphmethod
AT ruiyunliu measuringthetopologicaltimeirreversibilityoftimeserieswiththedegreevectorbasedvisibilitygraphmethod
AT yuchen measuringthetopologicaltimeirreversibilityoftimeserieswiththedegreevectorbasedvisibilitygraphmethod
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