IDEAS home Printed from https://ideas.repec.org/p/com/wpaper/003.html
   My bibliography  Save this paper

Meta-heuristic Methods for Outliers Detection in Multivariate Time Series

Author

Listed:
  • Domenico Cucina
  • Mattheos Protopapas
  • Antonietta di Salvatore

Abstract

In this article we use meta-heuristic methods to detect additive outliers in multivariate time series. The implemented algorithms are: simulated annealing, threshold accepting and two different versions of genetic algorithm. All of them use the same objective function, the generalized AIC-like criterion, and in contrast with many of the existing methods, they don't require to specify a vector ARMA model for the data and are able to detect any number of potential outliers simultaneously. We used simulated time series and real data to evaluate and compare the performance of the proposed methods.

Suggested Citation

  • Domenico Cucina & Mattheos Protopapas & Antonietta di Salvatore, 2008. "Meta-heuristic Methods for Outliers Detection in Multivariate Time Series," Working Papers 003, COMISEF.
    • Handle: RePEc:com:wpaper:003
    as

    Download full text from publisher

    File URL: http://comisef.eu/files/wps003.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Winker, Peter & Gilli, Manfred, 2004. "Applications of optimization heuristics to estimation and modelling problems," Computational Statistics & Data Analysis, Elsevier, vol. 47(2), pages 211-223, September.
    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. Blueschke-Nikolaeva, V. & Blueschke, D. & Neck, R., 2012. "Optimal control of nonlinear dynamic econometric models: An algorithm and an application," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3230-3240.
    2. Giorgio Fagiolo & Mattia Guerini & Francesco Lamperti & Alessio Moneta & Andrea Roventini, 2017. "Validation of Agent-Based Models in Economics and Finance," LEM Papers Series 2017/23, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    3. Manfred Gilli & Enrico Schumann, 2012. "Heuristic optimisation in financial modelling," Annals of Operations Research, Springer, vol. 193(1), pages 129-158, March.
    4. S.-C. Horng & S.-Y. Lin, 2009. "Ordinal Optimization of G/G/1/K Polling Systems with k-Limited Service Discipline," Journal of Optimization Theory and Applications, Springer, vol. 140(2), pages 213-231, February.
    5. Lin, D.K.J. & Sharpe, C. & Winker, P., 2010. "Optimized U-type designs on flexible regions," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1505-1515, June.
    6. Savin, Ivan & Egbetokun, Abiodun, 2016. "Emergence of innovation networks from R&D cooperation with endogenous absorptive capacity," Journal of Economic Dynamics and Control, Elsevier, vol. 64(C), pages 82-103.
    7. Baragona Roberto & Cucina Domenico, 2013. "Multivariate Self-Exciting Threshold Autoregressive Modeling by Genetic Algorithms," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 233(1), pages 3-21, February.
    8. Chipman, J. & Winker, P., 2005. "Optimal aggregation of linear time series models," Computational Statistics & Data Analysis, Elsevier, vol. 49(2), pages 311-331, April.
    9. Florios, Kostas, 2018. "A hyperplanes intersection simulated annealing algorithm for maximum score estimation," Econometrics and Statistics, Elsevier, vol. 8(C), pages 37-55.
    10. Lyra, M. & Paha, J. & Paterlini, S. & Winker, P., 2010. "Optimization heuristics for determining internal rating grading scales," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2693-2706, November.
    11. Manuel Rizzo & Francesco Battaglia, 2016. "On the Choice of a Genetic Algorithm for Estimating GARCH Models," Computational Economics, Springer;Society for Computational Economics, vol. 48(3), pages 473-485, October.
    12. Ivan Savin & Peter Winker, 2012. "Heuristic Optimization Methods for Dynamic Panel Data Model Selection: Application on the Russian Innovative Performance," Computational Economics, Springer;Society for Computational Economics, vol. 39(4), pages 337-363, April.
    13. Makram El-Shagi, 2011. "An evolutionary algorithm for the estimation of threshold vector error correction models," International Economics and Economic Policy, Springer, vol. 8(4), pages 341-362, December.
    14. Manfred Gilli & Enrico Schumann, 2010. "Calibrating Option Pricing Models with Heuristics," Working Papers 030, COMISEF.
    15. Baur, Dirk G. & Glover, Kristoffer J., 2014. "Heterogeneous expectations in the gold market: Specification and estimation," Journal of Economic Dynamics and Control, Elsevier, vol. 40(C), pages 116-133.
    16. Peter Winker & Marianna Lyra & Chris Sharpe, 2011. "Least median of squares estimation by optimization heuristics with an application to the CAPM and a multi-factor model," Computational Management Science, Springer, vol. 8(1), pages 103-123, April.
    17. Unler, Alper & Murat, Alper, 2010. "A discrete particle swarm optimization method for feature selection in binary classification problems," European Journal of Operational Research, Elsevier, vol. 206(3), pages 528-539, November.
    18. Kapetanios, George, 2007. "Variable selection in regression models using nonstandard optimisation of information criteria," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 4-15, September.
    19. Yang, Zheng & Tian, Zheng & Yuan, Zixia, 2007. "GSA-based maximum likelihood estimation for threshold vector error correction model," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 109-120, September.
    20. S. Bandyopadhyay & R. Baragona & U. Maulik, 2010. "Fuzzy clustering of univariate and multivariate time series by genetic multiobjective optimization," Working Papers 028, COMISEF.

    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:com:wpaper:003. 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: Anil Khuman (email available below). General contact details of provider: http://www.comisef.eu .

    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.