An advanced hidden Markov model for hourly rainfall time series

Hidden Markov model segmentation of hydrological and enviromental time series

Motivated by Hubert's segmentation procedure [16, 17], we discuss the application of hidden Markov models (HMM) to the segmentation of hydrological and enviromental time series. We use a HMM algorithm which segments time series of several hundred terms in a few seconds and is compu-tationally feasible for even longer time series. The segmentation algorithm computes the Maximum Likelihood segmen...

Non-homogeneous hidden Markov-switching models for wind time series

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A Hidden Markov Model-based Forecasting Model for Fuzzy Time Series

Vague and incomplete data represented as linguistic values massively exists in diverse real-word applications. The task of forecasting fuzzy time series under uncertain circumstances is thus of great important but difficult. The inherent uncertainty involving time evolution usually makes the transition of states in a system probabilistic. In this paper, we proposed a new forecasting model based...

Clustering Time Series with Hidden Markov Modelsand Dynamic Time

Clustering financial time series: New insights from an extended hidden Markov model

In recent years, large amounts of financial data have become available for analysis. We propose exploring returns from 21 European stock markets by model-based clustering of regime switching models. These econometric models identify clusters of time series with similar dynamic patterns and moreover allow relaxing assumptions of existing approaches, such as the assumption of conditional Gaussian...