Using missing dispersion patterns to detect determinism and nonlinearity in time series data
Abstract Kulp et al. proposed the number of missing ordinal patterns as a test statistic to detect the nonlinearity of time series in 2017. Inspired by the article, we propose a novel method called the number of missing dispersion patterns (NMDPs), which refers to the number of dispersion patterns that do not appear in the series after being symbolized using the Rostaghi and Azami method. The proposed method can distinguish between deterministic and stochastic dynamics well. By comparing the statistical difference between the NMDP results of the original series and the NMDP results of the wavelet iterative amplitude adjustment Fourier transform surrogate series, NMDP is demonstrated as a useful statistic for testing series nonlinearity. In this paper, we first apply NMDP to the model time series and verify its effectiveness as a test for determinism as well as nonlinearity and its ability to distinguish noise-contaminated series from purely stochastic series. We then apply NMDP to different stock index time series. The experiment results show that our method may be a suitable tool for quantifying the complexity of the inherent structure of the market under different development levels..
| Medienart: |
Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:111 |
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| Enthalten in: |
Nonlinear dynamics - 111(2022), 1 vom: 23. Sept., Seite 439-458 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Zhou, Qin [VerfasserIn] |
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| Links: |
Volltext [lizenzpflichtig] |
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| Themen: |
Determinism |
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| Anmerkungen: |
© The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| doi: |
10.1007/s11071-022-07835-3 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a Abstract Kulp et al. proposed the number of missing ordinal patterns as a test statistic to detect the nonlinearity of time series in 2017. Inspired by the article, we propose a novel method called the number of missing dispersion patterns (NMDPs), which refers to the number of dispersion patterns that do not appear in the series after being symbolized using the Rostaghi and Azami method. The proposed method can distinguish between deterministic and stochastic dynamics well. By comparing the statistical difference between the NMDP results of the original series and the NMDP results of the wavelet iterative amplitude adjustment Fourier transform surrogate series, NMDP is demonstrated as a useful statistic for testing series nonlinearity. In this paper, we first apply NMDP to the model time series and verify its effectiveness as a test for determinism as well as nonlinearity and its ability to distinguish noise-contaminated series from purely stochastic series. We then apply NMDP to different stock index time series. The experiment results show that our method may be a suitable tool for quantifying the complexity of the inherent structure of the market under different development levels. | ||
| 650 | 4 | |a Missing dispersion patterns | |
| 650 | 4 | |a Determinism | |
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| 700 | 1 | |a Zhang, Boyi |4 aut | |
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