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Abstract Details
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Activity Number:
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567
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Type:
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Contributed
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Date/Time:
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Wednesday, August 1, 2012 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Computing
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| Abstract - #306376 |
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Title:
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Semiparametric Forecasting of Nonlinear Temporal Processes
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Author(s):
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Jane Harvill*+ and Nalini Ravishanker
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Companies:
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Baylor University and University of Connecticut
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Address:
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Department of Statistical Science, Waco, TX, 76798-7140, United States
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Keywords:
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Spline-backfitting ;
Nonlinear time series ;
Forecasting ;
Oracle estimation ;
Solar power
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Abstract:
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The curse of dimensionality has been problematic in the application of nonparametric and semiparametric regression techniques to high-dimensional time series data. Spline-backfitted local linear (SBLL) and spline-backfitted kernel (SBK) estimators have been successful in addressing this problem, and provide computationally efficient estimators. Moreover, under fairly weak conditions, the estimators are point-wise asymptotically normal. Little work has been conducted in investigating the properties of forecasts using models estimated via SBLL or SBK methods. We propose a method for SBLL and SBK forecasting, and investigate the properties of those forecasts. For illustration, we apply the forecasting methods to irradiance data collected from a solar power plant in Lanai, Hawaii provided by Sandia Research Laboratories.
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