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HMMCont (version 1.0)

Hidden Markov Model for Continuous Observations Processes

Description

The package includes the functions designed to analyse continuous observations processes with the Hidden Markov Model approach. They include Baum-Welch and Viterbi algorithms and additional visualisation functions. The observations are assumed to have Gaussian distribution and to be weakly stationary processes. The package was created for analyses of financial time series, but can also be applied to any continuous observations processes.

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Version

Install

install.packages('HMMCont')

Monthly Downloads

9

Version

1.0

License

GPL-3

Maintainer

Mikhail Beketov

Last Published

February 11th, 2014

Functions in HMMCont (1.0)

viterbicont

Viterbi Algorithm
logreturns

Calculating Log-returns
Prices

A dummy data set of prices.
hmmsetcont

Setting an initial HMM object
baumwelchcont

Baum-Welch Algorithm
hmmcontSimul

Simulation of an observation and underlying Markov processes according to a given model
statesDistributionsPlot

Probability Density Functions of the States