# predict

0th

Percentile

##### Predict a Future State

Predict a state or the probability distribution over states in $n$ time steps.

Keywords
models
##### Usage
"predict"(object, current_state = NULL, n=1,
probabilities = FALSE, randomized = FALSE, prior=FALSE)
##### Arguments
object
an "EMM"/"TRACDS" object.
current_state
use a specified current state. If NULL, the EMM's current state is used.
n
number of time steps.
probabilities
if TRUE, instead of the predicted state, the probability distribution is returned.
randomized
if TRUE, the predicted state is choosen randomly with a selection probability proportional to its transition probability
prior
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.
##### Details

Prediction is done using $A^n$ where $A$ is the transition probability matrix maintained by the EMM. Random tie-breaking is used.

##### Value

The name of the predicted state or a vector with the probability distribution over all states.

transition_matrix

##### Aliases
• predict
• predict,TRACDS-method
##### Examples
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)

Documentation reproduced from package rEMM, version 1.0-11, License: GPL-2

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