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Comparison of time series with unequal length in the frequency domain

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  • Caiado, Jorge
  • Crato, Nuno
  • Peña, Daniel

Abstract

In statistical data analysis it is often important to compare, classify, and cluster different time series. For these purposes various methods have been proposed in the literature, but they usually assume time series with the same sample size. In this paper, we propose a spectral domain method for handling time series of unequal length. The method make the spectral estimates comparable by producing statistics at the same frequency. The procedure is compared with other methods proposed in the literature by a Monte Carlo simulation study. As an illustrative example, the proposed spectral method is applied to cluster industrial production series of some developed countries.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 15310.

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Date of creation: Apr 2009
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Handle: RePEc:pra:mprapa:15310

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Keywords: Autocorrelation function; Cluster analysis; Interpolated periodogram; Reduced periodogram; Spectral analysis; Time series; Zero-padding.;

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References

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  1. Camacho, Maximo & Pérez-Quirós, Gabriel & Sáiz Matute, Lorena, 2005. "Are European Business Cycles Close Enough to be Just One?," CEPR Discussion Papers, C.E.P.R. Discussion Papers 4824, C.E.P.R. Discussion Papers.
  2. Caiado, Jorge & Crato, Nuno & Pena, Daniel, 2006. "A periodogram-based metric for time series classification," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 50(10), pages 2668-2684, June.
  3. Maharaj, Elizabeth Ann, 2002. "Comparison of non-stationary time series in the frequency domain," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 40(1), pages 131-141, July.
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Cited by:
  1. Jin, Lei, 2011. "A data-driven test to compare two or multiple time series," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 55(6), pages 2183-2196, June.
  2. Jorge Caiado & Nuno Crato, 2010. "Identifying common dynamic features in stock returns," Quantitative Finance, Taylor & Francis Journals, Taylor & Francis Journals, vol. 10(7), pages 797-807.
  3. Jentsch, Carsten & Pauly, Markus, 2012. "A note on using periodogram-based distances for comparing spectral densities," Statistics & Probability Letters, Elsevier, Elsevier, vol. 82(1), pages 158-164.
  4. Maharaj, Elizabeth Ann & D’Urso, Pierpaolo, 2010. "A coherence-based approach for the pattern recognition of time series," Physica A: Statistical Mechanics and its Applications, Elsevier, Elsevier, vol. 389(17), pages 3516-3537.
  5. Otranto, Edoardo, 2010. "Identifying financial time series with similar dynamic conditional correlation," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 54(1), pages 1-15, January.
  6. Preuß, Philip & Hildebrandt, Thimo, 2013. "Comparing spectral densities of stationary time series with unequal sample sizes," Statistics & Probability Letters, Elsevier, Elsevier, vol. 83(4), pages 1174-1183.

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