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Some advances in non‐linear and adaptive modelling in time‐series
Author(s) -
Tiao George C.,
Tsay Ruey S.
Publication year - 1994
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.3980130206
Subject(s) - computer science , series (stratigraphy) , linear model , component (thermodynamics) , inference , bayesian probability , time series , gibbs sampling , focus (optics) , bayesian inference , econometrics , machine learning , artificial intelligence , mathematics , paleontology , physics , optics , biology , thermodynamics
This paper considers some recent developments in non‐linear and linear time series analysis. It consists of two main components. The first emphasizes the advances in non‐linear modelling and in Bayesian inference via the Gibbs sampler. Advantages and the usefulness of these advances are illustrated by real examples. The second component is concerned with adaptive forecasting. This shows that linear models can provide accurate forecasts provided that the parameters involved are estimated adaptively. In particular, we focus on forecasting long‐memory time series. Again, a real example is used to illustrate the results.