predict.covlmc {mixvlmc}

Next state prediction in a discrete time series for a VLMC with covariates

Description

This function computes one step ahead predictions for a discrete time series based on a VLMC with covariates.

Usage

## S3 method for class 'covlmc'
predict(
  object,
  newdata,
  newcov,
  type = c("raw", "probs"),
  final_pred = TRUE,
  ...
)

Arguments

object

a fitted covlmc object.

newdata

a time series adapted to the covlmc object.

newcov

a data frame with the new values for the covariates.

type

character indicating the type of prediction required. The default "raw" returns actual predictions in the form of a new time series. The alternative "probs" returns a matrix of prediction probabilities (see details).

final_pred

if TRUE (default value), the predictions include a final prediction step, made by computing the context of the full time series. When FALSE this final prediction is not included.

...

additional arguments.

Details

Given a time series X, at time step t, a context is computed using observations from X[1] to X[t-1] (see the dedicated section). The prediction is then the most probable state for X[t] given this logistic model of the context and the corresponding values of the covariates. The time series of predictions is returned by the function when type="raw" (default case).

When type="probs", the function returns of the probabilities of each state for X[t] as estimated by the logistic models. Those probabilities are returned as a matrix of probabilities with column names given by the state names.

Value

A vector of predictions if type="raw" or a matrix of state probabilities if type="probs".

Extended contexts

As explained in details in loglikelihood.covlmc() documentation and in the dedicated vignette("likelihood", package = "mixvlmc"), the first initial values of a time series do not in general have a proper context for a COVLMC with a non zero order. In order to predict something meaningful for those values, we rely on the notion of extended context defined in the documents mentioned above. This follows the same logic as using loglikelihood.covlmc() with the parameter initial="extended". All covlmc functions that need to manipulate initial values with no proper context use the same approach.

Examples

pc <- powerconsumption[powerconsumption$week == 10, ]
dts <- cut(pc$active_power, breaks = c(0, quantile(pc$active_power, probs = c(0.2, 0.7, 1))))
dts_cov <- data.frame(day_night = (pc$hour >= 7 & pc$hour <= 17))
m_cov <- covlmc(dts, dts_cov, min_size = 5, alpha = 0.5)
dts_probs <- predict(m_cov, dts[1:144], dts_cov[1:144, , drop = FALSE], type = "probs")
dts_preds <- predict(m_cov, dts[1:144], dts_cov[1:144, , drop = FALSE],
  type = "raw", final_pred = FALSE
)