Multi-scale and hidden resolution time series models

Multi-Scale and Hidden Resolution Time Series Models

We introduce a class of multi-scale models for time series. The novel framework couples standard linear models at different levels of resolution via stochastic links across scales. Jeffrey’s rule of conditioning is used to revise the implied distributions and ensure that the probability distributions at the different levels are strictly compatible. This results in a new class of models for time...

Integration of multi-time-scale models in time series forecasting

A solution to the problem of producing long-range forecasts on a short sampling interval is proposed. It involves the incorporation of information from a long sampling interval series, which could come from an independent source, into forecasts produced by a state-space model based on a short sampling interval. The solution is motivated by the desire to incorporate yearly electricity consumptio...

A Class of Multi-Scale Time Series Models

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 use...

Application of Hidden Markov Models and Hidden Semi-Markov Models to Financial Time Series

Time Series Classiication Using Hidden Markov Models and Neural Networks

| This paper gives a brief overview on statistical classiication of time series using Hidden Markov Models. To overcome some limitations and assumptions made in the Hidden Markov Model framework two successful hybrid extensions using Neural Networks are presented. At rst, a short description of traditional stochastic-based Hidden Markov Model classiiers with diierent kinds of emission probabili...