ModelEvaluationFunction: Automated ARIMA model selection according to flexible...

In snarf-snarf/karma: Complementary tools for automated time-series machine learning.

Description

Automated ARIMA model selection according to flexible optimisation criteria.

Usage

ModelEvaluationFunction(sol, y, diffs_ = 0, metric = "MAPE", fixed_ = T,
  xreg = NULL, cv_ = "out", test_pct = 20, test_type = "percentage",
  plot = T)

Arguments

sol	ARIMA order in the form of a candidate solution to the optimisation problem.
y	A univariate time-series vector; type <numeric> or <ts>.
diffs_	Differencing step: Indicates whether the candidate solution needs to be differenced for stationarity (and to what degree); 0,1,...,n; type <int>.
metric	Choose a model validation metric that will be used as the main optimisation criterion during model selection.
fixed_	Fixed term flag. Indicate whether the fixed term option in Arima() needs to be switched on during model selection; T, F; type <logical>.
xreg	Optional vector or matrix of exogenous regressors; see documentation for Arima(), package 'forecast'.
cv_	Choose cross-validation dataset to be used during model selection; "out": Performance of out-of-sample forecast (classic train/test split) will be used for model validation; "in": Performance of in-sample forecast (classic parametric regression type of validation) will be used for model validation.
test_pct	Percentage of train-test split in cross-validation (e.g. 70-30).
test_type	Train-test split type, i.e. percentage or fixed window; "percentage": test_pct = 12 will be read as the 12 percent of the length of the series; "window": test_pct = 12 will be read as the 12 last time points (e.g. months) of the series.
ar_terms	Autoregressive terms; can be in the form of a vector (fixed_ = FALSE) or a list of vectors (fixed_ = TRUE).
ma_terms	Move average terms; can be in the form of a vector (fixed_ = FALSE) or a list of vectors (fixed_ = TRUE).

Value

Object of class "karma.fit"; (extends class "Arima" from package 'forecast').

See Also

tseries, forecast

Examples

sol = list(ar_terms=c(1,2), ma_terms=c(3,4)) 
ModelEvaluationFunction(y, sol, 0)

snarf-snarf/karma documentation built on May 24, 2019, 7:19 a.m.