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Recursive forecasting, smoothing and seasonal adjustment of non‐stationary environmental data
Author(s) -
Young P. C.,
Ng C. N.,
Lane K.,
Parker D.
Publication year - 1991
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.3980100105
Subject(s) - smoothing , computer science , variable (mathematics) , suite , series (stratigraphy) , time series , exploit , exponential smoothing , anomaly (physics) , algorithm , econometrics , data mining , mathematics , machine learning , geology , mathematical analysis , paleontology , physics , computer security , archaeology , condensed matter physics , computer vision , history
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 micro CAPTAIN computer program. The practical utility of the analysis is demonstrated by an example based on the analysis of atmospheric CO 2 and sea surface temperature anomaly data.