HMMCont: Hidden Markov Model for Continuous Observations Processes
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.
- Mikhail A. Beketov
- Date of publication
- 2014-02-11 17:15:51
- Mikhail A. Beketov <firstname.lastname@example.org>
- Baum-Welch Algorithm
- Hidden Markov Model for Continuous Observations Processes
- Simulation of an observation and underlying Markov processes...
- Setting an initial HMM object
- Calculating Log-returns
- A dummy data set of prices.
- Probability Density Functions of the States
- Viterbi Algorithm
Files in this package