pls function

Prediction Likelihood Score

Function estimates Prediction Likelihood Score for the provided model

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.

Returns

A value of the log-likelihood.

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.

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)

References

• distribution. IEEE Signal Processing Letters. 13 (5): 300-303. 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..

Author(s)

Ivan Svetunkov, ivan@svetunkov.com