adam_params: Tuning Parameters for ADAM Models

In modeltime: The Tidymodels Extension for Time Series Modeling

Tuning Parameters for ADAM Models

Description

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.

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.

Value

A dials parameter

A parameter

A parameter

A parameter

A parameter

A parameter

A parameter

A parameter

A parameter

A parameter

Examples

use_constant()

regressors_treatment()

distribution()

modeltime documentation built on Oct. 23, 2024, 1:07 a.m.