viterbicont: Viterbi Algorithm
In HMMCont: Hidden Markov Model for Continuous Observations Processes

Description Usage Arguments Value Author(s) References See Also Examples

The function performs Viterbi algorithm (Viterbi, 1967). It can be applied to a ContObservHMM object after sufficient number of Baum-welch iterations (function baumwelchcont).

1	viterbicont(hmm)

hmm

An object of the class ContObservHMM.

An object of the class ContObservHMM (see section on the function hmmsetcont). The object can be analysed with the class-specific functions print, summary, and plot.

Mikhail A. Beketov

Viterbi, A.J. 1967. Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Transactions on Information Theory. 13: 260-269.

Functions: hmmsetcont, baumwelchcont, and statesDistributionsPlot.

Returns<-logreturns(Prices) # Getting a stationary process
Returns<-Returns*10 		# Scaling the values
hmm<-hmmsetcont(Returns) 	# Creating a HMM object
for(i in 1:6){hmm<-baumwelchcont(hmm)} # Baum-Welch is 
# executed 6 times and results are accumulated

hmmcomplete<-viterbicont(hmm) # Viterbi execution

par(mfrow=c(2,1))
plot(hmmcomplete, Prices, ylabel="Price") 
plot(hmmcomplete, ylabel="Returns") # the revealed 
# Markov chain and the observations are plotted