Reveals How HMMs Can Be Used as General-Purpose Time Series ModelsImplements all
methods in RHidden Markov Models for Time Series: An Introduction Using R applies …

Sequence analysis is being more and more widely used for the analysis of social sequences
and other multivariate categorical time series data. However, it is often complex to describe …

This paper provides a tutorial on key issues in hidden Markov modeling. Hidden Markov
models have become very popular models for time series and longitudinal data in recent …

Discrete-valued time series are common in practice, but methods for their analysis are not
well-known. In recent years, methods have been developed which are specifically designed …

In the search for useful models for discrete-valued time series, one possible approach is to
take a standard model for continuous-valued series, for example, a Gaussian autoregres …

Hidden Markov Models (HMMs) are one of the most fundamental and widely used statistical
tools for modeling discrete time series. In general, learning HMMs from data is …

Hidden Markov models reproduce most of the stylized facts about daily series of returns. A
notable exception is the inability of the models to reproduce one ubiquitous feature of such …

In this thesis, we develop an extension of the Hidden Markov Model (HMM) that addresses
two of the most important challenges of financial time series modeling: non-stationary and …

Markov chains have increasingly become useful way of capturing stochastic nature of many
economic and financial variables. Although the hidden Markov processes have been widely …

Hidden Markov models are often used to model daily returns and to infer the hidden state of
financial markets. Previous studies have found that the estimated models change over time …