Is there any R package that supports fitting an HMM using multiple sequences of observations? to the best of my knowledge depmixS4 does not support this feature


No, depmixS4 supports multiple external variables to be included to forecast underlying time series. In this case transition matrix is a function of all the other external variables. It is given in depmix S4 vignette also.-

a useful material for start - https://machinelearningstories.blogspot.com/2017/02/hidden-markov-model-session-1.html &

R Code snippets-

Required library


data loading-

physician_prescrition_data <-c(12,16,45,45,56,67,78,98,120,124,156)

model execution-

HMM_model <- depmixS4::depmix(physician_prescrition_data~1, nstates = 2,ntimes=length(physician_prescrition_data))

model fitting

HMM_fm <- fit(HMM_model)

Transition probabilties-


posterior states-

posterior(HMM_fm) plot(ts(posterior(HMM_fm)[,1]))

Emission probabilties-


  • $\begingroup$ In model execution, instead of ' physician_prescrition_data~1' , one has to write physician_prescrition_data ~ all external variable. $\endgroup$ – Arpit Sisodia Aug 13 '18 at 5:54

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