Predict a Future State
Predict a state or the probability distribution over states in $n$ time steps.
"predict"(object, current_state = NULL, n=1, probabilities = FALSE, randomized = FALSE, prior=FALSE)
- use a specified current state.
NULL, the EMM's current state is used.
- number of time steps.
TRUE, instead of the predicted state, the probability distribution is returned.
TRUE, the predicted state is choosen randomly with a selection probability proportional to its transition probability
- add one to each transition count. This is equal to starting with a uniform prior for the transition count distribution, i.e. initially all transitions are equally likely. It also prevents the product of probabilities to be zero if a transition was never observed.
Prediction is done using $A^n$ where $A$ is the transition probability matrix maintained by the EMM. Random tie-breaking is used.
The name of the predicted state or a vector with the probability
distribution over all states.
data("EMMTraffic") emm <- EMM(measure="eJaccard", threshold=0.2) emm <- build(emm, EMMTraffic) #plot(emm) ## plot graph ## Predict state starting an state 1 after 1, 2 and 100 time intervals ## Note, state 7 is an absorbing state. predict(emm, n=1, current_state="1") predict(emm, n=2, current_state="1") predict(emm, n=100, current_state="1") ## Get probability distribution predict(emm, n=2, current_state="1", probabilities = TRUE)
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