decode_states: Decode the underlying hidden state sequence

In fHMM: Fitting Hidden Markov Models to Financial Data

Decode the underlying hidden state sequence

Description

This function decodes the (most likely) underlying hidden state sequence by applying the Viterbi algorithm for global decoding.

Usage

decode_states(x, verbose = TRUE)

viterbi(observations, nstates, sdd, Gamma, mu, sigma = NULL, df = NULL)

Arguments

x	[fHMM_model] An object of class fHMM_model.
verbose	[logical(1)] Set to TRUE to print progress messages.
observations	[numeric()] A numeric vector of state-dependent observations.
nstates	[integer(1)] The number of states.
sdd	[character(1)] A character, specifying the state-dependent distribution. One of "normal" (the normal distribution), "lognormal" (the log-normal distribution), "t" (the t-distribution), "gamma" (the gamma distribution), "poisson" (the Poisson distribution).
Gamma	[matrix()] A transition probability matrix of dimension nstates.
mu	[numeric(nstates)] A numeric vector of expected values for the state-dependent distribution in the different states of length nstates. For the gamma- or Poisson-distribution, mu must be positive.
sigma	[NULL | numeric(nstates)] A positive numeric vector of standard deviations for the state-dependent distribution in the different states of length nstates. Not relevant in case of a state-dependent Poisson distribution.
df	[NULL | numeric(nstates)] A positive numeric vector of degrees of freedom for the state-dependent distribution in the different states of length nstates. Only relevant in case of a state-dependent t-distribution.

Value

An object of class fHMM_model with decoded state sequence included.

References

https://en.wikipedia.org/wiki/Viterbi_algorithm

Examples

decode_states(dax_model_3t)
plot(dax_model_3t, type = "ts")
viterbi(
  observations = c(1, 1, 1, 10, 10, 10),
  nstates      = 2,
  sdd          = "poisson",
  Gamma        = matrix(0.5, 2, 2),
  mu           = c(1, 10)
)

fHMM documentation built on May 5, 2026, 5:07 p.m.