Description Usage Arguments Value See Also Examples
Automated ARIMA model selection according to flexible optimisation criteria.
1 2 3 | ModelEvaluationFunction(sol, y, diffs_ = 0, metric = "MAPE", fixed_ = T,
xreg = NULL, cv_ = "out", test_pct = 20, test_type = "percentage",
plot = T)
|
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). |
Object of class "karma.fit"; (extends class "Arima" from package 'forecast').
1 2 | sol = list(ar_terms=c(1,2), ma_terms=c(3,4))
ModelEvaluationFunction(y, sol, 0)
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