# 6 Continuous-time HMMs

#### Jan-Ole Fischer

This vignette has not yet been updated to work with RTMB

> Before diving into this vignette, we recommend reading the vignette Introduction to LaMa.

The regular HMM formulation needs a key assumption to be applicable, namely the data need to be observed at regular, equidistant time-points such that the transition probabilities can be interpreted meaningfully w.r.t. a specific time unit. If this is not the case, the model used should account for this by building on a mathematical formulation in continuous time. The obvious choice here is to retain most of the HMM model formulation, but replace the unobserved discrete-time Markov chain with a continuous-time Markov chain. However, here it is important to note that the so-called snapshot property needs to be fulfilled, i.e. the observed process at time \(t\) can only depend on the state at that time instant and not on the interval since the previous observation. For more details see Glennie et al. (2023).

A continuous-time Markov chain is characterised by a so-called (infinitesimal) generator matrix \[ Q = \begin{pmatrix} q_{11} & q_{12} & \cdots & q_{1N} \\ q_{21} & q_{22} & \cdots & q_{2N} \\ \vdots & \vdots & \ddots & \vdots \\ q_{N1} & q_{N2} & \cdots & q_{NN} \\ \end{pmatrix}, \] where the diagonal entries are \(q_{ii} = - \sum_{j \neq i} q_{ij}\), \(q_{ij} \geq 0\) for \(i \neq j\). This matrix can be interpreted as the derivative of the transition probability matrix and completely describes the dynamics of the state process. The time-spent in a state \(i\) is exponentially distributed with rate \(-q_{ii}\) and conditional on leaving the state, the probability to transition to a state \(j \neq i\) is \(\omega_{ij} = q_{ij} / -q_{ii}\). For a more detailed introduction see Dobrow (2016) (pp. 265 f.). For observation times \(t_1\) and \(t_2\), we can then obtain the transition probability matrix between these points via the identity \[ \Gamma(t_1, t_2) = \exp(Q (t_2 - t_1)), \] where \(\exp()\) is the matrix exponential. This follows from the so-called Kolmogorov forward equations, but for more details see Dobrow (2016).

## Example 1: two states

### Setting parameters for simulation

    # loading the package
    library(LaMa)
    #> Loading required package: RTMB

We start by setting parameters to simulate data. In this example, state 1 has a smaller rate and the state dwell time in state one follows and \(Exp(0.5)\) distribution, i.e. it exhibits longer dwell times than state 2 with rate 1.

    # generator matrix Q:
    Q = matrix(c(-0.5, 0.5, 1, -1),
               nrow = 2, byrow = TRUE)

    # parameters for the state-dependent (normal) distributions
    mu = c(5, 20)
    sigma = c(2, 5)

### Simulating data

We simulate the continuous-time Markov chain by drawing the exponentially distributed state dwell-times. Within a stay, we can assume whatever structure we like for the observation times, as these are not explicitly modeled. Here we choose to generate them by a Poisson process with rate \(\lambda=1\), but this choice is arbitrary. For more details on Poisson point processes, see the MM(M)PP vignette.

    set.seed(123)

    k = 200 # number of state switches
    trans_times = s = rep(NA, k) # time points where the chain transitions
    s[1] = sample(1:2, 1) # initial distribution c(0.5, 0.5)
    # exponentially distributed waiting times
    trans_times[1] = rexp(1, -Q[s[1],s[1]])
    n_arrivals = rpois(1, trans_times[1])
    obs_times = sort(runif(n_arrivals, 0, trans_times[1]))
    x = rnorm(n_arrivals, mu[s[1]], sigma[s[1]])
    for(t in 2:k){
      s[t] = c(1,2)[-s[t-1]] # for 2-states, always a state switch when transitioning
      # exponentially distributed waiting times
      trans_times[t] = trans_times[t-1] + rexp(1, -Q[s[t], s[t]])
      n_arrivals = rpois(1, trans_times[t]-trans_times[t-1])
      obs_times = c(obs_times,
                    sort(runif(n_arrivals, trans_times[t-1], trans_times[t])))
      x = c(x, rnorm(n_arrivals, mu[s[t]], sigma[s[t]]))
    }

Let’s visualise the simulated continuous-time HMM:

    color = c("orange", "deepskyblue")

    n = length(obs_times)
    plot(obs_times[1:50], x[1:50], pch = 16, bty = "n", xlab = "observation times",
         ylab = "x", ylim = c(-5,25))
    segments(x0 = c(0,trans_times[1:48]), x1 = trans_times[1:49],
             y0 = rep(-5,50), y1 = rep(-5,50), col = color[s[1:49]], lwd = 4)
    legend("topright", lwd = 2, col = color,
           legend = c("state 1", "state 2"), box.lwd = 0)

### Writing the negative log-likelihood function

The likelihood of a continuous-time HMM for observations \(x_{t_1}, \dots, x_{t_T}\) at irregular time points \(t_1, \dots, t_T\) has the exact same structure as the regular HMM likelihood: \[ L(\theta) = \delta^{(1)} \Gamma(t_1, t_2) P(x_{t_2}) \Gamma(t_2, t_3) P(x_{t_3}) \dots \Gamma(t_{T-1}, t_T) P(x_{t_T}) 1^t, \] where \(\delta^{(1)}\), \(P\) and \(1^t\) are as usual and \(\Gamma(t_k, t_{k+1})\) is computed as explained above. Thus we can fit such models using the standard implementation of the general forward algorithm `forward_g()` with time-varying transition probability matrices. We can use the `generator()` function to compute the infinitesimal generator matrix from an unconstrained parameter vector and `tpm_cont()` to compute all matrix exponentials.

    nll = function(par, timediff, x, N){
      mu = par[1:N]
      sigma = exp(par[N+1:N])
      Q = generator(par[2*N+1:(N*(N-1))]) # generator matrix
      Pi = stationary_cont(Q) # stationary dist of CT Markov chain
      Qube = tpm_cont(Q, timediff) # this computes exp(Q*timediff)
      allprobs = matrix(1, nrow = length(x), ncol = N)
      ind = which(!is.na(x))
      for(j in 1:N){
        allprobs[ind,j] = dnorm(x[ind], mu[j], sigma[j])
      }
      -forward_g(Pi, Qube, allprobs)
    }

### Fitting a continuous-time HMM to the data

    par = c(mu = c(5, 15), # state-dependent means
            logsigma = c(log(3), log(5)), # state-dependent sds
            qs = c(log(1), log(0.5))) # off-diagonals of Q

    timediff = diff(obs_times)

    system.time(
      mod <- nlm(nll, par, timediff = timediff, x = x, N = 2)
    )
    #>    user  system elapsed
    #>   0.098   0.003   0.101

### Results

    N = 2
    # mu
    round(mod$estimate[1:N],2)
    #> [1]  5.06 20.24
    # sigma
    round(exp(mod$estimate[N+1:N]))
    #> [1] 2 5
    Q = generator(mod$estimate[2*N+1:(N*(N-1))])
    round(Q,3)
    #>        S1     S2
    #> S1 -0.479  0.479
    #> S2  0.905 -0.905

## Example 2: three states

### Setting parameters for simulation

    # generator matrix Q:
    Q = matrix(c(-0.5,0.2,0.3,
                 1,-2, 1,
                 0.4, 0.6, -1), nrow = 3, byrow = TRUE)

    # parameters for the state-dependent (normal) distributions
    mu = c(5, 15, 30)
    sigma = c(2, 3, 5)

### Simulating data

The simulation is very similar but we now also have to draw which state to transition to, as explained in the beginning.

    set.seed(123)

    k = 200 # number of state switches
    trans_times = s = rep(NA, k) # time points where the chain transitions
    s[1] = sample(1:3, 1) # uniform initial distribution
    # exponentially distributed waiting times
    trans_times[1] = rexp(1, -Q[s[1],s[1]])
    n_arrivals = rpois(1, trans_times[1])
    obs_times = sort(runif(n_arrivals, 0, trans_times[1]))
    x = rnorm(n_arrivals, mu[s[1]], sigma[s[1]])
    for(t in 2:k){
      # off-diagonal elements of the s[t-1] row of Q divided by the diagonal element
      # give the probabilities of the next state
      s[t] = sample(c(1:3)[-s[t-1]], 1, prob = Q[s[t-1],-s[t-1]]/-Q[s[t-1],s[t-1]])
      # exponentially distributed waiting times
      trans_times[t] = trans_times[t-1] + rexp(1, -Q[s[t], s[t]])
      n_arrivals = rpois(1, trans_times[t]-trans_times[t-1])
      obs_times = c(obs_times,
                    sort(runif(n_arrivals, trans_times[t-1], trans_times[t])))
      x = c(x, rnorm(n_arrivals, mu[s[t]], sigma[s[t]]))
    }

### Fitting a 3-state continuous-time HMM to the data

    par = c(mu = c(5, 10, 25), # state-dependent means
            logsigma = c(log(2), log(2), log(6)), # state-dependent sds
            qs = rep(0, 6)) # off-diagonals of Q

    timediff = diff(obs_times)

    system.time(
      mod2 <- nlm(nll, par, timediff = timediff, x = x, N = 3, stepmax = 10)
    )
    #>    user  system elapsed
    #>   0.411   0.010   0.422
    # without restricting stepmax, we run into numerical problems

### Results

    N = 3
    # mu
    round(mod2$estimate[1:N],2)
    #> [1]  4.90 15.45 29.10
    # sigma
    round(exp(mod2$estimate[N+1:N]),2)
    #> [1] 1.80 2.58 5.06
    Q = generator(mod2$estimate[2*N+1:(N*(N-1))])
    round(Q, 3)
    #>        S1     S2     S3
    #> S1 -0.888  0.565  0.323
    #> S2  2.821 -3.469  0.647
    #> S3  0.000  0.770 -0.770

> Continue reading with Markov-modulated Poisson processes.

## References

Dobrow, Robert P. 2016. Introduction to Stochastic Processes with r. John Wiley & Sons.
Glennie, Richard, Timo Adam, Vianey Leos-Barajas, Théo Michelot, Theoni Photopoulou, and Brett T McClintock. 2023. “Hidden Markov Models: Pitfalls and Opportunities in Ecology.” Methods in Ecology and Evolution 14 (1): 43–56.