Caiado, Jorge and Crato, Nuno and Peña, Daniel (2007): Comparison of time series with unequal length.
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Abstract
The comparison and classification of time series is an important issue in practical time series analysis. For these purposes, various methods have been proposed in the literature, but all have shortcomings, especially when the observed time series have different sample sizes. In this paper, we propose spectral domain methods for handling time series of unequal length. The methods make the spectral estimates comparable, by producing statistics at the same frequency. A first sensible approach may consist on zero-padding the shorter time series in order to increase the corresponding number of periodogram ordinates. We show that this works well provided the sample sizes are not very different, but does not give good results in case the time series lengths are very unbalanced. For this latter case, we study some periodogram-based comparison methods and construct a test. Both the methods and the test display reasonable properties for series of any lengths. Additionally and for reference, we develop a parametric comparison method. The procedures are assessed by a Monte Carlo simulation study. As an illustrative example, a periodogram method is used to compare and cluster industrial production series of some developed countries.
Item Type: | MPRA Paper |
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Original Title: | Comparison of time series with unequal length |
Language: | English |
Keywords: | Cluster analysis; Interpolated periodogram; Reduced periodogram; Spectral analysis; Time series; Zero-padding |
Subjects: | C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C32 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models C - Mathematical and Quantitative Methods > C0 - General C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C12 - Hypothesis Testing: General |
Item ID: | 6605 |
Depositing User: | Jorge Caiado |
Date Deposited: | 07 Jan 2008 04:29 |
Last Modified: | 26 Sep 2019 10:25 |
References: | Caiado, J., Crato, N. and Peña, D. (2006). "A periodogram-based metric for time series classification", Computational Statistics & Data Analysis, 50, 2668-2684. Camacho, M., Pérez-Quiróz, G. and Saiz, L. (2004). "Are European business cycles close enough to be just one?", Bank of Spain, Working Paper n.º 0408. Coates, D. S. and Diggle, P. J. (1986). "Tests for comparing two estimated spectral densities", Journal of Time Series Analysis, 7, 7-20. Dargahi-Noubary, G. R. (1992). "Discrimination between Gaussian time series based on their spectral differences", Communications in Statistics: Theory and Methods, 21, 2439-2458. Davies, R. B. and Harte, D. S. (1987). "Tests for Hurst effect", Biometrika, 74, 95-102. Diggle, P. J. and Fisher, N. I. (1991). "Nonparametric comparison of cumulative periodograms", Applied Statistics, 40, 423-434. Hamilton, J. D. (1994). Time Series Analysis. Princeton University Press, Princeton NJ. Jenkins, G. M. and Priestley, M. B. (1957). "The spectral analysis of time series", Journal of the Royal Statistical Society Series B , 19, 1-12. Johnson, R. and Wichern, D. (2002). Applied Multivariate Statistical Analysis. 5th Ed., Prentice-Hall. Maharaj, E. A. (1996). "A significance test for classifying ARMA models", Journal of Statistical Computing and Simulation, 54, 305-331. Maharaj, E. A. (2002). "Comparison of non-stationary time series in the frequency domain", Computational Statistics & Data Analysis, 40, 131-141. Peña, D. and Rodriguez, J. (2005). "Detecting non linearity in time series by model selection criteria", International Journal of Forecasting, 21, 731-748. Quinn, B. G. (2006). "Statistical methods of spectrum change detection", Digital Signal Processing, 16, 588-596. Wang, N. and Blostein, S. (2004). "Adaptive zero-padding OFDM over frequency-selective multipath channels", Journal on Applied Signal Processing, 10, 1478-1488. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/6605 |