We introduce a class of multi-scale models for time series. The novel framework couples 'simple' standard Markov models for the time series stochastic process at different levels of aggregation, and links them via 'error' models to induce a new and rich class of structured linear models reconciling modeling and information at different levels of resolution. Jeffrey's rule of conditioning is used to revise the implied distributions and ensure that the probability distributions at different levels are strictly compatible. Our construction has several interesting characteristics: a variety of autocorrelation functions resulting from just a few parameters; the ability to combine information from different scales; and the capacity to emulate long memory processes. There are at least three uses for our multi-scale framework: to integrate the information from data observed at different scales; to induce a particular process when the data is observed only at the finest scale; as a prior for an underlying multi-scale process. Bayesian estimation based on MCMC analysis and forecasting based on simulation are developed. Two interesting applications are presented: in the first application, we illustrate some basic concepts of our multi-scale class of models through the analysis of a series of potential hydroelectric energy; In the second application we use our multi-scale framework to model a series of land temperatures of the northern hemisphere. Keywords: Autoregressive models; Bayesian inference; Information aggregation; Jeffrey's Rule of Conditioning; Multi-Scale stochastic models; Multi-Scale time series models

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