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Recursive estimation and forecasting of non‐stationary time series
Author(s) -
Ng C. N.,
Young P. C.
Publication year - 1990
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.3980090208
Subject(s) - series (stratigraphy) , univariate , computer science , smoothing , multivariable calculus , time series , state space , order of integration (calculus) , state space representation , singular spectrum analysis , mathematical optimization , exponential smoothing , identification (biology) , mathematics , algorithm , econometrics , multivariate statistics , machine learning , statistics , singular value decomposition , paleontology , mathematical analysis , control engineering , engineering , computer vision , biology , botany
The paper presents a unified, fully recursive approach to the modelling and forecasting of non‐stationary time‐series. The basic time‐series model, which is based on the well‐known ‘component’ or ‘structuraL’ form, is formulated in state‐space terms. A novel spectral decomposition procedure, based on the exploitation of recursive smoothing algorithms, is then utilized to simplify the procedures of model identification and estimation. Finally, the fully recursive formulation allows for conventional or self‐adaptive implementation of state‐space forecasting and seasonal adjustment. Although the paper is restricted to the consideration of univariate time series, the basic approach can be extended to handle explanatory variables or full multivariable (vector) series.