Tuning Parameters for ADAM Models

Usage

ets_model(values = c("ZZZ", "XXX", "YYY", "CCC", "PPP", "FFF"))

loss(
  values = c("likelihood", "MSE", "MAE", "HAM", "LASSO", "RIDGE", "TMSE", "GTMSE",
    "MSEh", "MSCE")
)

use_constant(values = c(FALSE, TRUE))

regressors_treatment(values = c("use", "select", "adapt"))

outliers_treatment(values = c("ignore", "use", "select"))

probability_model(
  values = c("none", "auto", "fixed", "general", "odds-ratio", "inverse-odds-ratio",
    "direct")
)

distribution(
  values = c("default", "dnorm", "dlaplace", "ds", "dgnorm", "dlnorm", "dinvgauss",
    "dgamma")
)

information_criteria(values = c("AICc", "AIC", "BICc", "BIC"))

select_order(values = c(FALSE, TRUE))

Arguments

values: A character string of possible values.

Value

A dials parameter

A parameter

Details

The main parameters for ADAM models are:

ets_model:
- model="ZZZ" means that the model will be selected based on the chosen information criteria type. The Branch and Bound is used in the process.
- model="XXX" means that only additive components are tested, using Branch and Bound.
- model="YYY" implies selecting between multiplicative components.
- model="CCC" triggers the combination of forecasts of models using information criteria weights (Kolassa, 2011).
- combinations between these four and the classical components are also accepted. For example, model="CAY" will combine models with additive trend and either none or multiplicative seasonality.
- model="PPP" will produce the selection between pure additive and pure multiplicative models. "P" stands for "Pure". This cannot be mixed with other types of components.
- model="FFF" will select between all the 30 types of models. "F" stands for "Full". This cannot be mixed with other types of components.
- The parameter model can also be a vector of names of models for a finer tuning (pool of models). For example, model=c("ANN","AAA") will estimate only two models and select the best of them.
loss:
- likelihood - the model is estimated via the maximization of the likelihood of the function specified in distribution;
- MSE (Mean Squared Error),
- MAE (Mean Absolute Error),
- HAM (Half Absolute Moment),
- LASSO - use LASSO to shrink the parameters of the model;
- RIDGE - use RIDGE to shrink the parameters of the model;
- TMSE - Trace Mean Squared Error,
- GTMSE - Geometric Trace Mean Squared Error,
- MSEh - optimisation using only h-steps ahead error,
- MSCE - Mean Squared Cumulative Error.
non_seasonal_ar: The order of the non-seasonal auto-regressive (AR) terms.
non_seasonal_differences: The order of integration for non-seasonal differencing.
non_seasonal_ma: The order of the non-seasonal moving average (MA) terms.
seasonal_ar: The order of the seasonal auto-regressive (SAR) terms.
seasonal_differences: The order of integration for seasonal differencing.
seasonal_ma: The order of the seasonal moving average (SMA) terms.
use_constant: Logical, determining, whether the constant is needed in the model or not.
regressors_treatment: The variable defines what to do with the provided explanatory variables.
outliers_treatment: Defines what to do with outliers.
probability_model: The type of model used in probability estimation.
distribution: What density function to assume for the error term.
information_criteria: The information criterion to use in the model selection / combination procedure.
select_order: If TRUE, then the function will select the most appropriate order.

Examples

use_constant()
#> Logical, determining, whether the constant is needed in the model or not  (qualitative)
#> 2 possible values include:
#> FALSE and TRUE 

regressors_treatment()
#> The variable defines what to do with the provided explanatory variables.  (qualitative)
#> 3 possible values include:
#> 'use', 'select' and 'adapt' 

distribution()
#> What density function to assume for the error term.  (qualitative)
#> 8 possible values include:
#> 'default', 'dnorm', 'dlaplace', 'ds', 'dgnorm', 'dlnorm', 'dinvgauss' and 'dg...