pmml.ARIMA: Generate PMML for an ARIMA object the *forecast* package.

In pmml: Generate PMML for Various Models

Generate PMML for an ARIMA object the forecast package.

Description

Generate PMML for an ARIMA object the forecast package.

Usage

## S3 method for class 'ARIMA'
pmml(
  model,
  model_name = "ARIMA_model",
  app_name = "SoftwareAG PMML Generator",
  description = "ARIMA Time Series Model",
  copyright = NULL,
  model_version = NULL,
  transforms = NULL,
  missing_value_replacement = NULL,
  ts_type = "statespace",
  cpi_levels = c(80, 95),
  ...
)

Arguments

model	An ARIMA object from the package forecast.
model_name	A name to be given to the PMML model.
app_name	The name of the application that generated the PMML.
description	A descriptive text for the Header element of the PMML.
copyright	The copyright notice for the model.
model_version	A string specifying the model version.
transforms	Data transformations.
missing_value_replacement	Value to be used as the 'missingValueReplacement' attribute for all MiningFields.
ts_type	The type of time series representation for PMML: "arima" or "statespace".
cpi_levels	Vector of confidence levels for prediction intervals.
...	Further arguments passed to or from other methods.

Details

The model is represented as a PMML TimeSeriesModel.

When ts_type = "statespace" (by default), the R object is exported as StateSpaceModel in PMML.

When ts_type = "arima", the R object is exported as ARIMA in PMML with conditional least squares (CLS). Note that ARIMA models in R are estimated using a state space representation. Therefore, when using CLS with seasonal models, forecast results between R and PMML may not match exactly. Additionally, when ts_type="arima", prediction intervals are exported for non-seasonal models only. For ARIMA models with d=2, the prediction intervals between R and PMML may not match.

OutputField elements are exported with dataType "string", and contain a collection of all values up to and including the steps-ahead value supplied during scoring. String output in this form is facilitated by Extension elements in the PMML file, and is supported by Zementis Server since version 10.6.0.0.

cpi_levels behaves similar to levels in forecast::forecast: values must be between 0 and 100, non-inclusive.

Models with a drift term will be supported in a future version.

Transforms are currently not supported for ARIMA models.

Value

PMML representation of the ARIMA object.

Author(s)

Dmitriy Bolotov

Examples

## Not run: 
library(forecast)

# non-seasonal model
data("WWWusage")
mod <- Arima(WWWusage, order = c(3, 1, 1))
mod_pmml <- pmml(mod)

# seasonal model
data("JohnsonJohnson")
mod_02 <- Arima(JohnsonJohnson,
  order = c(1, 1, 1),
  seasonal = c(1, 1, 1)
)
mod_02_pmml <- pmml(mod_02)

# non-seasonal model exported with Conditional Least Squares
data("WWWusage")
mod <- Arima(WWWusage, order = c(3, 1, 1))
mod_pmml <- pmml(mod, ts_type = "arima")

## End(Not run)

pmml documentation built on March 18, 2022, 5:49 p.m.