Bayesian Identification, Extraction and Forecasting of Unobserved Components for Time Series in the Frequency Domains

Abstract

This work aims to present a full Bayesian framework to identify, extract and forecast unobserved components in time series. The major novelty is to present a probabilistic framework to analyze the identification conditions. More precisely, informative prior distributions are assigned to the spectral densities of the unobserved components. This entails a… (More)

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