IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v433y2015icp291-303.html
   My bibliography  Save this article

Assessment of the relative ratio of correlated and uncorrelated waiting times in the Southern California earthquakes catalogue

Author

Listed:
  • Matcharashvili, Teimuraz
  • Chelidze, Tamaz
  • Zhukova, Natalia

Abstract

In the present study, we investigated the interevent time interval distribution of earthquakes in Southern California. We analyzed and compared datasets of waiting times between consecutive earthquakes and time structure-distorted datasets. The aim of this study was to determine the proportion of waiting time values in the original catalogue that can be regarded as statistically distinguishable or indistinguishable from the baseline time intervals datasets where the original structure of the temporal distribution of earthquakes was disrupted. We compiled two types of time structure-distorted baseline sequences, which comprised mean values of: (a) shuffled original interevent data and (b) interevent times data from time-randomized catalogues. To compare the dynamical characteristics of the original and time structure-distorted baseline sequences, we used several data analysis methods such as power spectrum regression, detrended fluctuation, and multifractal detrended fluctuation analysis. We also tested the nonlinear structure of the original and baseline sequences using the magnitude and sign scaling analysis method. We calculated theZscore in order to assess whether the interevent time values in the original dataset shared statistical similarity or dissimilarity with the time structure-distorted baseline interevent data sequence. We compared the interevent time values in the original dataset with the mean baseline interevent times computed for two types of time structure-distorted sequences. The results showed that about 30% of the original interevent times were statistically indistinguishable from the mean of the shuffled dataset and 10% from the mean of the time structure-distorted baseline interevent dataset. We performed similar analyses for other catalogues obtained from different parts of the world. According to the results of this analysis, the proportion of events in the original catalogues that were indistinguishable from sequences with disturbed time structure did not contradict the results obtained for the Southern California catalogue.

Suggested Citation

  • Matcharashvili, Teimuraz & Chelidze, Tamaz & Zhukova, Natalia, 2015. "Assessment of the relative ratio of correlated and uncorrelated waiting times in the Southern California earthquakes catalogue," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 433(C), pages 291-303.
    • Handle: RePEc:eee:phsmap:v:433:y:2015:i:c:p:291-303
    DOI: 10.1016/j.physa.2015.03.060
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437115003222
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2015.03.060?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Plamen Ch. Ivanov & Ainslie Yuen & Boris Podobnik & Youngki Lee, 2004. "Common Scaling Patterns in Intertrade Times of U. S. Stocks," Papers cond-mat/0403662, arXiv.org.
    2. Ashkenazy, Yosef & Havlin, Shlomo & Ivanov, Plamen Ch. & Peng, Chung-K. & Schulte-Frohlinde, Verena & Stanley, H.Eugene, 2003. "Magnitude and sign scaling in power-law correlated time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 323(C), pages 19-41.
    3. Kantelhardt, Jan W. & Zschiegner, Stephan A. & Koscielny-Bunde, Eva & Havlin, Shlomo & Bunde, Armin & Stanley, H.Eugene, 2002. "Multifractal detrended fluctuation analysis of nonstationary time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 316(1), pages 87-114.
    4. Giulio Di Toro & David L. Goldsby & Terry E. Tullis, 2004. "Friction falls towards zero in quartz rock as slip velocity approaches seismic rates," Nature, Nature, vol. 427(6973), pages 436-439, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lahmiri, Salim, 2017. "On fractality and chaos in Moroccan family business stock returns and volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 29-39.
    2. Matcharashvili, Teimuraz & Zhukova, Natalia & Chelidze, Tamaz & Founda, Dimitra & Gerasopoulos, Evangelos, 2017. "Analysis of long-term variation of the annual number of warmer and colder days using Mahalanobis distance metrics — A case study for Athens," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 487(C), pages 22-31.
    3. Lahmiri, Salim, 2016. "Clustering of Casablanca stock market based on hurst exponent estimates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 310-318.
    4. Hayat, Umar & Barkat, Adnan & Ali, Aamir & Rehman, Khaista & Sifat, Shazia & Iqbal, Talat, 2019. "Fractal analysis of shallow and intermediate-depth seismicity of Hindu Kush," Chaos, Solitons & Fractals, Elsevier, vol. 128(C), pages 71-82.
    5. Matcharashvili, T. & Chelidze, T. & Javakhishvili, Z. & Zhukova, N., 2016. "Variation of the scaling characteristics of temporal and spatial distribution of earthquakes in Caucasus," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 449(C), pages 136-144.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zunino, L. & Tabak, B.M. & Figliola, A. & Pérez, D.G. & Garavaglia, M. & Rosso, O.A., 2008. "A multifractal approach for stock market inefficiency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(26), pages 6558-6566.
    2. Ni, Xiao-Hui & Jiang, Zhi-Qiang & Gu, Gao-Feng & Ren, Fei & Chen, Wei & Zhou, Wei-Xing, 2010. "Scaling and memory in the non-Poisson process of limit order cancelation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(14), pages 2751-2761.
    3. Ruan, Yong-Ping & Zhou, Wei-Xing, 2011. "Long-term correlations and multifractal nature in the intertrade durations of a liquid Chinese stock and its warrant," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(9), pages 1646-1654.
    4. Marcin Wątorek & Jarosław Kwapień & Stanisław Drożdż, 2022. "Multifractal Cross-Correlations of Bitcoin and Ether Trading Characteristics in the Post-COVID-19 Time," Future Internet, MDPI, vol. 14(7), pages 1-15, July.
    5. Ducha, F.A. & Atman, A.P.F. & Bosco de Magalhães, A.R., 2021. "Information flux in complex networks: Path to stylized facts," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).
    6. Argyroudis, G. & Siokis, F., 2018. "The complexity of the HANG SENG Index and its constituencies during the 2007–2008 Great Recession," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 463-474.
    7. Lu, Feiyu & Yuan, Naiming & Fu, Zuntao & Mao, Jiangyu, 2012. "Universal scaling behaviors of meteorological variables’ volatility and relations with original records," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(20), pages 4953-4962.
    8. Yu, Zhongde & Huang, Yu & Fu, Zuntao, 2020. "Nonlinear strength quantifier based on phase correlation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    9. Mahmoodi, Korosh & West, Bruce J. & Grigolini, Paolo, 2020. "On the dynamical foundation of multifractality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    10. Martín-Montoya, L.A. & Aranda-Camacho, N.M. & Quimbay, C.J., 2015. "Long-range correlations and trends in Colombian seismic time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 124-133.
    11. Lavička, Hynek & Kracík, Jiří, 2020. "Fluctuation analysis of electric power loads in Europe: Correlation multifractality vs. Distribution function multifractality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    12. Kakinaka, Shinji & Umeno, Ken, 2021. "Exploring asymmetric multifractal cross-correlations of price–volatility and asymmetric volatility dynamics in cryptocurrency markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    13. Longfeng Zhao & Wei Li & Andrea Fenu & Boris Podobnik & Yougui Wang & H. Eugene Stanley, 2017. "The q-dependent detrended cross-correlation analysis of stock market," Papers 1705.01406, arXiv.org, revised Jun 2017.
    14. Vitanov, Nikolay K. & Sakai, Kenshi & Dimitrova, Zlatinka I., 2008. "SSA, PCA, TDPSC, ACFA: Useful combination of methods for analysis of short and nonstationary time series," Chaos, Solitons & Fractals, Elsevier, vol. 37(1), pages 187-202.
    15. Scalas, Enrico & Kaizoji, Taisei & Kirchler, Michael & Huber, Jürgen & Tedeschi, Alessandra, 2006. "Waiting times between orders and trades in double-auction markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 366(C), pages 463-471.
    16. El Alaoui, Marwane & Benbachir, Saâd, 2013. "Multifractal detrended cross-correlation analysis in the MENA area," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(23), pages 5985-5993.
    17. Méndez-Gordillo, Alma Rosa & Cadenas, Erasmo, 2021. "Wind speed forecasting by the extraction of the multifractal patterns of time series through the multiplicative cascade technique," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    18. Nagarajan, Radhakrishnan & Kavasseri, Rajesh G., 2005. "Minimizing the effect of periodic and quasi-periodic trends in detrended fluctuation analysis," Chaos, Solitons & Fractals, Elsevier, vol. 26(3), pages 777-784.
    19. Diniz-Maganini, Natalia & Diniz, Eduardo H. & Rasheed, Abdul A., 2021. "Bitcoin’s price efficiency and safe haven properties during the COVID-19 pandemic: A comparison," Research in International Business and Finance, Elsevier, vol. 58(C).
    20. Mei, Dexiang & Liu, Jing & Ma, Feng & Chen, Wang, 2017. "Forecasting stock market volatility: Do realized skewness and kurtosis help?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 481(C), pages 153-159.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:433:y:2015:i:c:p:291-303. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.