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Recursive forecasting, smoothing and seasonal adjustment of non‐stationary environmental data

Recursive forecasting, smoothing and seasonal adjustment of non‐stationary environmental data The paper presents a unified, fully recursive approach to the modelling, forecasting and seasonal adjustment of non‐stationary time series and shows how it can be used as a flexible tool in the analysis of environmental data. The approach is based on time‐variable parameter (TVP) versions of various well‐known time‐series models and exploits the suite of novel, recursive filtering and fixed interval smoothing algorithms available in the microCAPTAIN computer program. The practical utility of the analysis is demonstrated by an example based on the analysis of atmospheric CO2 and sea surface temperature anomaly data. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Forecasting Wiley

Recursive forecasting, smoothing and seasonal adjustment of non‐stationary environmental data

Journal of Forecasting , Volume 10 (1‐2) – Jan 1, 1991

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References (43)

Publisher
Wiley
Copyright
Copyright © 1991 John Wiley & Sons, Ltd.
ISSN
0277-6693
eISSN
1099-131X
DOI
10.1002/for.3980100105
Publisher site
See Article on Publisher Site

Abstract

The paper presents a unified, fully recursive approach to the modelling, forecasting and seasonal adjustment of non‐stationary time series and shows how it can be used as a flexible tool in the analysis of environmental data. The approach is based on time‐variable parameter (TVP) versions of various well‐known time‐series models and exploits the suite of novel, recursive filtering and fixed interval smoothing algorithms available in the microCAPTAIN computer program. The practical utility of the analysis is demonstrated by an example based on the analysis of atmospheric CO2 and sea surface temperature anomaly data.

Journal

Journal of ForecastingWiley

Published: Jan 1, 1991

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