Author(s)

C.N. Ng & T.L. Van

Abstract

Recursive methods of time series analysis developed in recent years provide a natural approach to the estimation of models with time-variable parameters, and hence a useful tool for the study of environmental data. This paper presents a fully recursive approach to the modelling and adaptive forecasting of non- stationary air quality time series. The approach is based on time-variable parameter versions of various well-known time series models and exploits the suite of novel, recursive algorithms of the Kalman Filter. The observed series is decomposed into a simple additive "component" with each component model written in the state-space Gauss-Markov form, in which the model parameter variations are assumed to follow a "generali

Keywords