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Journal of Automation and Information Sciences
SJR: 0.238 SNIP: 0.464 CiteScore™: 0.27

ISSN Imprimir: 1064-2315
ISSN On-line: 2163-9337

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Journal of Automation and Information Sciences

DOI: 10.1615/JAutomatInfScien.v47.i10.10
pages 1-12

Model of Autocorrelative Function of Time Series with Strong Dependence

Natalya D. Pankratova
Institute for Applied Systems Analysis of National Technical University of Ukraine "Igor Sikorsky Kiev Polytechnic Institute", Kiev
Natalya G. Zrazhevska
Institute for Applied Systems Analysis of National Technical University of Ukraine "Kiev Polytechnic Institute", Kiev

RESUMO

The model based on the optimization problem solving to improve the Hurst parameter estimation for time series with long-range dependence is proposed. The model can be adapted depending on the ultimate goal of estimation. The proposed model was tested on artificially generated data with known characteristics and applied to determination of the Hurst parameters of time series of RTS incomes. Development of the new model is actual because of the fact that traditional Hurst parameter estimations [2] may have a long range of values in practical applications due to nonstationary effects.

Referências

  1. Palma W., Long-memory time series. Theory and methods., John Wiley & Sons, New Jersey, 2007.

  2. Taqqu M.S., Teverovsky V., Willinger W., Estimators for long-range dependence: An empirical study, Fractals, 1995, 785-798.

  3. Teverovsky V., Taqqu M.S., Semi-parametric graphical estimation techniques for long-memory data, Lecture Notes in Statistics, 1996, 115, 420-432.

  4. Teverovsky V., Taqqu M.S., Testing for long-range dependences in the presence of shifting mean or a slowly declining trend, using a variance-type estimator, Time Series Analysis, 1997, 18, No. 3.

  5. Beran J., Statistics for long-memory processes, Chapman and Hall, New York, 1994.

  6. Clegg R.G., A practical guide to measuring the Hurst parameter, International Journal of Simulation: Systems, Science & Technology, 2006, No. 2, 3-14.

  7. Baillie R.T., Bollerslev T., Mikkelsen H.O., Fractionally integrated generalized autoregressive conditional heteroskedasticity, Journal of Econometrics, 1996, No. 74, 3-30.

  8. Bidyuk P.I., Systems approach to forecast based on time series model, Systemni doslidzhennya ta informatsiyni tekhnologii, 2003, No. 3, 88-110.


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