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)
Figure 3: Posterior mean of the spectrum with 95% (blue dotted line) and 5% (red dotted line) quantiles for sunspot data. The normalization parameter is τY = σ̂ 2 Y /2π, the hyper-parameters are K = 120, M = 1, and the number of iterations is N = 60000.