This function takes an Exponential smoothing state space model created using the ets() function from the forecast package and returns a list-of-lists representing in valid PFA document that could be used for scoring.
# S3 method for ets
pfa(object, name = NULL, version = NULL, doc = NULL,
metadata = NULL, randseed = NULL, options = NULL, cycle_reset = TRUE,
...)an object of class "ets"
a character which is an optional name for the scoring engine
an integer which is sequential version number for the model
a character which is documentation string for archival purposes
a list of strings that is computer-readable documentation for
archival purposes
a integer which is a global seed used to generate all random numbers. Multiple scoring engines derived from the same PFA file have different seeds generated from the global one
a list with value types depending on option name
Initialization or runtime options to customize implementation
(e.g. optimization switches). May be overridden or ignored by PFA consumer
a logical indicating whether to reset the state back to the
last point of the trained model before forecasting or to continue cycling forward
through trend and seasonality with every new call to the engine. The default is
TRUE so that repeated calls yield the same forecast as repeated calls to
forecast.
additional arguments affecting the PFA produced
a list of lists that compose valid PFA document
model <- forecast::ets(USAccDeaths, model="ZZZ")
model_as_pfa <- pfa(model)
Run the code above in your browser using DataLab