Multiple hidden Markov models for categorical time series

Factorizing Markov Models for Categorical Time Series Prediction

During the last decade, recommender systems became a popular class of models for many commercial websites. One of the best state-of-the-art methods for recommender systems are Matrix and Tensor Factorization models. Besides, Markov Chain models are common for representing sequential data problems (e.g. categorical time series data). The item recommendation problem of recommender systems in fact...

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

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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...

Analysing Time Series Structure with Hidden Markov Models

This paper considers the problem of extracting the relationships between two time series in a non-linear non-stationary environment with Hidden Markov Models (HMMs). We describe an algorithm which is capable of identifying associations between variables. The method is applied both to synthetic data and real data. We show that HMMs are capable of modelling the oil drilling process and that they ...