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I have a series of univariate data and I want to fit a Hidden Markov Model on it using the depmixS4 package on R. My final goal is to predict the next k observations (let's say k = 10) for the data series. I am not really interested in predicting the new state (that is important, but not my final goal), but I want to predict the next values for the data series.

It is a snippet of code:

# My series
data = rnorm(10000)
df_1_col = data.frame(data)
colnames(df_1_col) <- c('obs')

mod <- depmix(obs ~ 1, data = draws, nstates = n_state)
fit.mod <- fit(mod)

At this point I don't know how to predict the next out-of-samples values. I would like something similar to the forecast function.

I try using the following code:

state_ests <- posterior(fit.mod)
pred_resp <- matrix(0, ncol = n_state, nrow = 10)

for(i in 1:n_state) {
  pred_resp[,i] <- predict(fit.mod@response[[i]][[1]])
}

Using this code the predict function generates a number of predicted values that is equal to the number of observations into data, so it is not right.

How can I do this quite basic operations? I am new to HMM, but I already tried to look into many resources and I did not find any information. Thanks :)

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