Abstract Details
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Activity Number:
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523
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 7, 2013 : 10:30 AM to 12:20 PM
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Sponsor:
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Business and Economic Statistics Section
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| Abstract - #309094 |
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Title:
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Instant Trend-Seasonal Decomposition of Time Series with Splines
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Author(s):
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Luis Francisco Rosales Marticorena*+
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Companies:
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Goettingen University
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Keywords:
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Penalized splines ;
Mixed model ;
Varying coecient ;
Correlated remainder
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Abstract:
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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 effectiveness of the approach.
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Authors who are presenting talks have a * after their name.
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