Multiscale Time Series
This chapter presents multiscale time series models that have been introduced by Ferreira et al. (2006). These models couple standard linear models at different time scales with linear link equations between scales. As in the multiscale random field models presented in Chapter 10, Jeffrey’s rule of conditioning is used to ensure that the processes at the different levels are compatible. This allows consistent modeling of time series at different levels of resolution (e.g., daily or monthly aggregates of financial or meteorological data) and also allows coherent combination of information across time scales.
KeywordsPosterior Distribution Markov Chain Monte Carlo Autocorrelation Function Autoregressive Model Memory Process
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