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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Autor*in:	Ryutaro Mori [verfasserIn] Ruiyun Liu [verfasserIn] Yu Chen [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	time series analysis time-reversibility visibility graph time series motifs time series similarity

Übergeordnetes Werk:	In: Frontiers in Physics - Frontiers Media S.A., 2014, 9(2021)
Übergeordnetes Werk:	volume:9 ; year:2021

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fphy.2021.777958

Katalog-ID:	DOAJ014500922

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spelling	10.3389/fphy.2021.777958 doi (DE-627)DOAJ014500922 (DE-599)DOAJ58bfc738c58b4abb952ebeaee66e1dc2 DE-627 ger DE-627 rakwb eng QC1-999 Ryutaro Mori verfasserin aut Measuring the Topological Time Irreversibility of Time Series With the Degree-Vector-Based Visibility Graph Method 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. time series analysis time-reversibility visibility graph time series motifs time series similarity Physics Ruiyun Liu verfasserin aut Yu Chen verfasserin aut In Frontiers in Physics Frontiers Media S.A., 2014 9(2021) (DE-627)750371749 (DE-600)2721033-9 2296424X nnns volume:9 year:2021 https://doi.org/10.3389/fphy.2021.777958 kostenfrei https://doaj.org/article/58bfc738c58b4abb952ebeaee66e1dc2 kostenfrei https://www.frontiersin.org/articles/10.3389/fphy.2021.777958/full kostenfrei https://doaj.org/toc/2296-424X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021
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allfieldsSound	10.3389/fphy.2021.777958 doi (DE-627)DOAJ014500922 (DE-599)DOAJ58bfc738c58b4abb952ebeaee66e1dc2 DE-627 ger DE-627 rakwb eng QC1-999 Ryutaro Mori verfasserin aut Measuring the Topological Time Irreversibility of Time Series With the Degree-Vector-Based Visibility Graph Method 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. time series analysis time-reversibility visibility graph time series motifs time series similarity Physics Ruiyun Liu verfasserin aut Yu Chen verfasserin aut In Frontiers in Physics Frontiers Media S.A., 2014 9(2021) (DE-627)750371749 (DE-600)2721033-9 2296424X nnns volume:9 year:2021 https://doi.org/10.3389/fphy.2021.777958 kostenfrei https://doaj.org/article/58bfc738c58b4abb952ebeaee66e1dc2 kostenfrei https://www.frontiersin.org/articles/10.3389/fphy.2021.777958/full kostenfrei https://doaj.org/toc/2296-424X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021
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callnumber-first	Q - Science
author	Ryutaro Mori
spellingShingle	Ryutaro Mori misc QC1-999 misc time series analysis misc time-reversibility misc visibility graph misc time series motifs misc time series similarity misc Physics Measuring the Topological Time Irreversibility of Time Series With the Degree-Vector-Based Visibility Graph Method
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topic_title	QC1-999 Measuring the Topological Time Irreversibility of Time Series With the Degree-Vector-Based Visibility Graph Method time series analysis time-reversibility visibility graph time series motifs time series similarity
topic	misc QC1-999 misc time series analysis misc time-reversibility misc visibility graph misc time series motifs misc time series similarity misc Physics
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title	Measuring the Topological Time Irreversibility of Time Series With the Degree-Vector-Based Visibility Graph Method
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title_full	Measuring the Topological Time Irreversibility of Time Series With the Degree-Vector-Based Visibility Graph Method
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title_sort	measuring the topological time irreversibility of time series with the degree-vector-based visibility graph method
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title_auth	Measuring the Topological Time Irreversibility of Time Series With the Degree-Vector-Based Visibility Graph Method
abstract	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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	Measuring the Topological Time Irreversibility of Time Series With the Degree-Vector-Based Visibility Graph Method
url	https://doi.org/10.3389/fphy.2021.777958 https://doaj.org/article/58bfc738c58b4abb952ebeaee66e1dc2 https://www.frontiersin.org/articles/10.3389/fphy.2021.777958/full https://doaj.org/toc/2296-424X
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