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Instant Trend-Seasonal Decomposition of Time Series with Splines

Author

Listed:
  • Luis Francisco Rosales

    (Georg-August-University Göttingen)

  • Tatyana Krivobokova

    (Georg-August-University Göttingen)

Abstract

We present a nonparametric method to decompose a times series into trend, seasonal and remainder components. This fully data-driven technique is based on penalized splines and makes an explicit characterization of the varying seasonality and the correlation in the remainder. The procedure takes advantage of the mixed model representation of penalized splines that allows for the simultaneous estimation of all model parameters from the corresponding likelihood. Simulation studies and three data examples illustrate the eff ectiveness of the approach.

Suggested Citation

  • Luis Francisco Rosales & Tatyana Krivobokova, 2012. "Instant Trend-Seasonal Decomposition of Time Series with Splines," Courant Research Centre: Poverty, Equity and Growth - Discussion Papers 131, Courant Research Centre PEG.
    • Handle: RePEc:got:gotcrc:131
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    Keywords

    Penalized splines; Mixed model; Varying coecient; Correlated remainder;
    All these keywords.

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