hmmsetcont: Setting an initial HMM object

In HMMCont: Hidden Markov Model for Continuous Observations Processes

Description

The function sets an initial Hidden Markov Model object with initial set of model parameters. It returns the object of class ContObservHMM that can be analysed with Baum-Welch (function baumwelchcont) and Viterbi algorithms (viterbicont).

Usage

hmmsetcont(Observations, Pi1 = 0.5, Pi2 = 0.5, A11 = 0.7, A12 = 0.3, 
A21 = 0.3, A22 = 0.7, Mu1 = 5, Mu2 = (-5), Var1 = 10, Var2 = 10)

## S3 method for class 'ContObservHMM'
print(x, ...)
## S3 method for class 'ContObservHMM'
summary(object, ...)
## S3 method for class 'ContObservHMM'
plot(x, Series=x$Observations, 
ylabel="Observation series", xlabel="Time", ...)

Arguments

Observations	Vector of observations (class "numeric"), a weakly stationary process (e.g. returns time series).
Pi1	Initial probability of state 1.
Pi2	Initial probability of state 2.
A11	Initial transition probability from state 1 to state 1.
A12	Initial transition probability from state 1 to state 2.
A21	Initial transition probability from state 2 to state 1.
A22	Initial transition probability from state 2 to state 2.
Mu1	Initial mean for Gaussian PDF for state 1.
Mu2	Initial mean for Gaussian PDF for state 2.
Var1	Initial variance for Gaussian PDF for state 1.
Var2	Initial variance for Gaussian PDF for state 2.
x	An object returned by the function hmmsetcont.
object	An object returned by the function hmmsetcont.
Series	Observations time series to be plotted along the Markov states.
ylabel	Y axis label.
xlabel	X axis label.
...	Not used.

Value

The function returns an object of the class ContObservHMM that is a list comprising the observations, tables accumulating the model parameters and results after each Baum-Welch iterations (i.e. after each execution of the function baumwelchcont), table for the state sequence derived by the Viterbi algorithm (function viterbicont), and table of the b-probabilities. The object can be analysed with the class-specific functions print, summary, and plot.

Author(s)

Mikhail A. Beketov

See Also

Functions: baumwelchcont, viterbicont, and statesDistributionsPlot.

Examples

Returns<-logreturns(Prices) # Getting a stationary process
Returns<-Returns*10 		# Scaling the values

hmm<-hmmsetcont(Returns) 	# Creating a HMM object
print(hmm) 					# Checking the initial parameters

for(i in 1:6){hmm<-baumwelchcont(hmm)} # Baum-Welch is 
# executed 6 times and results are accumulated
hmmcomplete<-viterbicont(hmm) # Viterbi execution
print(hmm) 		# Checking the accumulated parameters
summary(hmm) 	# Getting more detailed information
par(mfrow=c(2,1))
plot(hmmcomplete, Prices, ylabel="Price") 
plot(hmmcomplete, ylabel="Returns") # the revealed 
# Markov chain and the observations are plotted

HMMCont documentation built on May 1, 2019, 10:46 p.m.