Official websites use .gov
A .gov website belongs to an official government organization in the United States.
Secure .gov websites use HTTPS
A lock (
) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.
Topics
Data & Maps
Surveys & Programs
Resource Library
A Smoothness Priors Approach to the Modeling of Time Series with Trend and Seasonality
A Smoothness Priors Approach to the Modeling of Time Series with Trend and Seasonality
RR82-04
Abstract
A smoothness priors approach to the modeling of time series with trends and seasonality’s is shown. An observed time series is decomposed into local polynomial trend, seasonal, globally stationary autoregressive and observation error components. Each component is characterized by an unknown variance-white noise perturbed difference equation constraint. The constraints or Bayesian smoothness priors are expressed in state-space model form. A Kalman predictor yields the likelihood for the unknown variances (hyper parameters) with a computational complexity, O(N). Likelihoods are computed for different constraint order models in different subsets of constraint equation model classes. Akaike's minimum AIC procedure is used to select the best model fitted to the data within and between the alternative model classes. Smoothing is achieved by a smoother algorithm.
Others in Series
Working Paper
An Evaluation of Concurrent Adjustment on Census Bureau Time Series
An Evaluation of Concurrent Adjustment on Census Bureau Time Series
Working Paper
The Prediction of Time Series with Trends and Seasonalities
The Prediction of Time Series with Trends and Seasonalities
Working Paper
A Time-Varying Multivariate Autoregressive Modeling of Econometric ...
A Time-Varying Multivariate Autoregressive Modeling of Econometric Time Series
Related Information
WORKING PAPER
Statistical Research Reports and Studies
Page Last Revised - October 28, 2021
✕
Is this page helpful?
Yes
No
Yes
No✕
NO THANKS
255 characters maximum
255 characters maximum reached
255 characters maximum reached
✕
Thank you for your feedback.
Comments or suggestions?
Comments or suggestions?
