This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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424
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Type:
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Contributed
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Date/Time:
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Tuesday, August 3, 2010 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract - #309408 |
Title:
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Generalized Exponential Weighted Moving Average for Time Series Forecasting
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Author(s):
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Lu Wang*+
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Companies:
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University of California, Davis
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Address:
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4118 Mathematical Sciences Building, Davis, CA, 95616, U.S.
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Keywords:
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time series prediction ;
exponential smoothing ;
autoregressive integrated moving average ;
state-space model
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Abstract:
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We aim to provide a new methodology for predicting univariate time series and time series regression. This method is motivated by the ordinary exponential weighted moving average (EWMA). A general class of predictors based on a generalization that involving taking arbitrary linear combinations of EWMA estimators is constructed and is shown to be dense in the class of infinite order autoregressive process. Practical implementation of the method is simple after we recast the model in the usual Gaussian-Markov framework. We applied several strategies for model fitting and forecasting, e.g. stepwise regression, ridge regression, penalized least squares. Simulation and data analysis indicate that our proposed procedure may outperform the existing methods. Similar methodology has also been applied to time series regression with independent predictors.
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Authors who are presenting talks have a * after their name.
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