The Prediction of Time Series with Trends and Seasonalities

By Gersch, Will

Description
Statistical Reference Document

Excerpt
Introduction: In this paper we consider the optimal smoothing and forecasting of nonstationary time series with trend and seasonal mean value components with stationary covariance. Two classes of smoothness priors trend models are considered. In one the trend is modeled as a stochastically perturbed local polynomial function of time. In the other, the trend is assumed to consist of both the stocchastically perturbed local polynomial component plus a ?global? stationary time series component. A predictive likelihood interpretation of Akaike?s AIC is exploited to determine the best of the models from the alternative trend model classes, for best one-step-ahead and best-twelve-step-ahead prediction criteria. The smoothness priors approach to time series modeling was developed earlier in papers by Akaike and by us. The innovative step in this paper is the maximization of the expected entropy of the predictive distribution interpretation of the minimum AIC procedure. The modeling and smoothing of time series is ...

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Book Id: WPLBN0000597905
Format Type: PDF eBook
File Size: 1,343,538 KB.
Reproduction Date: 2005
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Title:

The Prediction of Time Series with Trends and Seasonalities

Author:

Gersch, Will

Volume:

Language:

English

Subject:

Government publications, Census., Census report

Collection:

U.S. Census Bureau Collection

Subcollection:

Historic
Publication Date:

Publisher:

U.S. Census Bureau Department

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