pls: Prediction Likelihood Score

In smooth: Forecasting Using State Space Models

Prediction Likelihood Score

Description

Function estimates Prediction Likelihood Score for the provided model

Usage

pls(object, holdout = NULL, ...)

## S3 method for class 'smooth'
pls(object, holdout = NULL, ...)

Arguments

object	The model estimated using smooth functions. This thing also accepts other models (e.g. estimated using functions from forecast package), but may not always work properly with them.
holdout	The values for the holdout part of the sample. If the model was fitted on the data with the holdout=TRUE, then the parameter is not needed.
...	Parameters passed to multicov function. The function is called in order to get the covariance matrix of 1 to h steps ahead forecast errors.

Details

Prediction likelihood score (PLS) is based on either normal or log-normal distribution of errors. This is extracted from the provided model. The likelihood based on the distribution of 1 to h steps ahead forecast errors is used in the process.

Value

A value of the log-likelihood.

Author(s)

Ivan Svetunkov, [e-mail]

References

distribution. IEEE Signal Processing Letters. 13 (5): 300-303. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1109/LSP.2006.870353")} - this is not yet used in the function.

• Snyder, R. D., Ord, J. K., Beaumont, A., 2012. Forecasting the intermittent demand for slow-moving inventories: A modelling approach. International Journal of Forecasting 28 (2), 485-496.

• Kolassa, S., 2016. Evaluating predictive count data distributions in retail sales forecasting. International Journal of Forecasting 32 (3), 788-803..

Examples

# Generate data, apply es() with the holdout parameter and calculate PLS
x <- rnorm(100,0,1)
ourModel <- es(x, h=10, holdout=TRUE)
pls(ourModel, type="a")
pls(ourModel, type="e")
pls(ourModel, type="s", obs=100, nsim=100)

smooth documentation built on June 22, 2024, 9:32 a.m.