Robust estimation of level and trend

Autor: G. Tunnicliffe Wilson, Mario Ferelli
Rok vydání: 1990
Předmět:
Zdroj: Journal of Forecasting. 9:151-172
ISSN: 1099-131X
0277-6693
DOI: 10.1002/for.3980090207
Popis: Numerical state space models are efficiently implemented for the estimation of the underlying level and trend of a time series. The model specification is chosen so that the estimation is insensitive to outliers yet adapts rapidly to step changes in level. An example illustrates, by means of projection plots, how at times of uncertainty in the evolution of the series the inferred distribution of level and trend may be multi-modal.
Databáze: OpenAIRE