IDEAS home Printed from https://ideas.repec.org/a/wly/jforec/v41y2022i1p167-180.html
   My bibliography  Save this article

Modeling interval trendlines: Symbolic singular spectrum analysis for interval time series

Author

Listed:
  • Miguel de Carvalho
  • Gabriel Martos

Abstract

In this article we propose an extension of singular spectrum analysis for interval‐valued time series. The proposed methods can be used to decompose and forecast the dynamics governing a set‐valued stochastic process. The resulting components on which the interval time series is decomposed can be understood as interval trendlines, cycles, or noise. Forecasting can be conducted through a linear recurrent method, and we devised generalizations of the decomposition method for the multivariate setting. The performance of the proposed methods is showcased in a simulation study. We apply the proposed methods so to track the dynamics governing the Argentina Stock Market (MERVAL) in real time, in a case study over a period of turbulence that led to discussions of the government of Argentina with the International Monetary Fund.

Suggested Citation

  • Miguel de Carvalho & Gabriel Martos, 2022. "Modeling interval trendlines: Symbolic singular spectrum analysis for interval time series," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 167-180, January.
    • Handle: RePEc:wly:jforec:v:41:y:2022:i:1:p:167-180
    DOI: 10.1002/for.2801
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/for.2801
    Download Restriction: no
    File URL: https://libkey.io/10.1002/for.2801?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
    ---><---

    References listed on IDEAS

    as
    1. Athanasios Orphanides & Simon van Norden, 2002. "The Unreliability of Output-Gap Estimates in Real Time," The Review of Economics and Statistics, MIT Press, vol. 84(4), pages 569-583, November.
    2. Paulo Rodrigues & Nazarii Salish, 2015. "Modeling and forecasting interval time series with threshold models," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 9(1), pages 41-57, March.
    3. Lin, Wei & González-Rivera, Gloria, 2016. "Interval-valued time series models: Estimation based on order statistics exploring the Agriculture Marketing Service data," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 694-711.
    4. Billard L. & Diday E., 2003. "From the Statistics of Data to the Statistics of Knowledge: Symbolic Data Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 470-487, January.
    5. de Carvalho, Miguel & Martos, Gabriel, 2020. "Brexit: Tracking and disentangling the sentiment towards leaving the EU," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1128-1137.
    6. Gloria González-Rivera & Wei Lin, 2013. "Constrained Regression for Interval-Valued Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(4), pages 473-490, October.
    7. de Carvalho, Miguel & Rodrigues, Paulo C. & Rua, António, 2012. "Tracking the US business cycle with a singular spectrum analysis," Economics Letters, Elsevier, vol. 114(1), pages 32-35.
    8. Michał Kisielewicz, 2013. "Stochastic Differential Inclusions," Springer Optimization and Its Applications, in: Stochastic Differential Inclusions and Applications, edition 127, chapter 0, pages 147-179, Springer.
    9. Rahim Mahmoudvand & Paulo Canas Rodrigues, 2018. "A new parsimonious recurrent forecasting model in singular spectrum analysis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(2), pages 191-200, March.
    10. Khan, M. Atikur Rahman & Poskitt, D.S., 2017. "Forecasting stochastic processes using singular spectrum analysis: Aspects of the theory and application," International Journal of Forecasting, Elsevier, vol. 33(1), pages 199-213.
    11. Sun, Yuying & Han, Ai & Hong, Yongmiao & Wang, Shouyang, 2018. "Threshold autoregressive models for interval-valued time series data," Journal of Econometrics, Elsevier, vol. 206(2), pages 414-446.
    12. Michał Kisielewicz, 2013. "Stochastic Differential Inclusions and Applications," Springer Optimization and Its Applications, Springer, edition 127, number 978-1-4614-6756-4, June.
    13. González-Rivera, Gloria & Arroyo, Javier, 2012. "Time series modeling of histogram-valued data: The daily histogram time series of S&P500 intradaily returns," International Journal of Forecasting, Elsevier, vol. 28(1), pages 20-33.
    Full references (including those not matched with items on IDEAS)

    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. Sun, Yuying & Zhang, Xinyu & Wan, Alan T.K. & Wang, Shouyang, 2022. "Model averaging for interval-valued data," European Journal of Operational Research, Elsevier, vol. 301(2), pages 772-784.
    2. Sun, Yuying & Han, Ai & Hong, Yongmiao & Wang, Shouyang, 2018. "Threshold autoregressive models for interval-valued time series data," Journal of Econometrics, Elsevier, vol. 206(2), pages 414-446.
    3. Buansing, T.S. Tuang & Golan, Amos & Ullah, Aman, 2020. "An information-theoretic approach for forecasting interval-valued SP500 daily returns," International Journal of Forecasting, Elsevier, vol. 36(3), pages 800-813.
    4. Gloria Gonzalez‐Rivera & Yun Luo & Esther Ruiz, 2020. "Prediction regions for interval‐valued time series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(4), pages 373-390, June.
    5. Juan Bógalo & Pilar Poncela & Eva Senra, 2021. "Circulant Singular Spectrum Analysis to Monitor the State of the Economy in Real Time," Mathematics, MDPI, vol. 9(11), pages 1-17, May.
    6. Lin, Wei & González-Rivera, Gloria, 2016. "Interval-valued time series models: Estimation based on order statistics exploring the Agriculture Marketing Service data," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 694-711.
    7. Babel Raïssa Guemdjo Kamdem & Jules Sadefo-Kamdem & Carlos Ougouyandjou, 2020. "On Random Extended Intervals and their ARMA Processes," Working Papers hal-03169516, HAL.
    8. Gloria Gonzalez-Rivera & Wei Lin, 2014. "Interval-valued Time Series: Model Estimation based on Order Statistics," Working Papers 201429, University of California at Riverside, Department of Economics.
    9. Chang, Meng-Shiuh & Ju, Peijie & Liu, Yilei & Hsueh, Shao-Chieh, 2022. "Determining hedges and safe havens for stocks using interval analysis," The North American Journal of Economics and Finance, Elsevier, vol. 61(C).
    10. Wei Lin & Gloria González‐Rivera, 2019. "Extreme returns and intensity of trading," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(7), pages 1121-1140, November.
    11. Sun, Yuying & Bao, Qin & Zheng, Jiali & Wang, Shouyang, 2020. "Assessing the price dynamics of onshore and offshore RMB markets: An ITS model approach," China Economic Review, Elsevier, vol. 62(C).
    12. Babel Raïssa Guemdjo Kamdem & Jules Sadefo-Kamdem & Carlos Ogouyandjou, 2021. "An Abelian Group way to study Random Extended Intervals and their ARMA Processes," Working Papers hal-03174631, HAL.
    13. Piao Wang & Shahid Hussain Gurmani & Zhifu Tao & Jinpei Liu & Huayou Chen, 2024. "Interval time series forecasting: A systematic literature review," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 249-285, March.
    14. Liang-Ching Lin & Hsiang-Lin Chien & Sangyeol Lee, 2021. "Symbolic interval-valued data analysis for time series based on auto-interval-regressive models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(1), pages 295-315, March.
    15. Jessada Tariboon & Sotiris K. Ntouyas & Bashir Ahmad & Ahmed Alsaedi, 2020. "Existence Results for Sequential Riemann–Liouville and Caputo Fractional Differential Inclusions with Generalized Fractional Integral Conditions," Mathematics, MDPI, vol. 8(6), pages 1-17, June.
    16. Josu Arteche & Javier García‐Enríquez, 2022. "Singular spectrum analysis for value at risk in stochastic volatility models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 3-16, January.
    17. de Carvalho, Miguel & Rua, António, 2017. "Real-time nowcasting the US output gap: Singular spectrum analysis at work," International Journal of Forecasting, Elsevier, vol. 33(1), pages 185-198.
    18. Ahmed Alsaedi & Ravi P. Agarwal & Sotiris K. Ntouyas & Bashir Ahmad, 2020. "Fractional-Order Integro-Differential Multivalued Problems with Fixed and Nonlocal Anti-Periodic Boundary Conditions," Mathematics, MDPI, vol. 8(10), pages 1-16, October.
    19. Sotiris K. Ntouyas & Bashir Ahmad & Jessada Tariboon, 2022. "( k , ψ )-Hilfer Nonlocal Integro-Multi-Point Boundary Value Problems for Fractional Differential Equations and Inclusions," Mathematics, MDPI, vol. 10(15), pages 1-20, July.
    20. Zachary Feinstein & Birgit Rudloff, 2015. "A Supermartingale Relation for Multivariate Risk Measures," Papers 1510.05561, arXiv.org, revised Jan 2018.

    More about this item

    Statistics

    Access and download statistics

    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:wly:jforec:v:41:y:2022:i:1:p:167-180. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/2966 .

    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.