model_specs-class: Setup base learning models
In tsensembler: Dynamic Ensembles for Time Series Forecasting

This class sets up the base learning models and respective parameters setting to learn the ensemble.

learner

character vector with the base learners to be trained. Currently available models are:

bm_gaussianprocess: Gaussian Process models, from the kernlab package. See gausspr for a complete description and possible parametrization. See bm_gaussianprocess for the function implementation.
bm_ppr: Projection Pursuit Regression models, from the stats package. See ppr for a complete description and possible parametrization. See bm_ppr for the function implementation.
bm_glm: Generalized Linear Models, from the glmnet package. See glmnet for a complete description and possible parametrization. See bm_glm for the function implementation.
bm_gbm: Generalized Boosted Regression models, from the gbm package. See gbm for a complete description and possible parametrization. See bm_gbm for the function implementation.
bm_randomforest: Random Forest models, from the ranger package. See ranger for a complete description and possible parametrization. See bm_randomforest for the function implementation.
bm_cubist: M5 tree models, from the Cubist package. See cubist for a complete description and possible parametrization. See bm_cubist for the function implementation.
bm_mars: Multivariate Adaptive Regression Splines models, from the earth package. See earth for a complete description and possible parametrization. See bm_mars for the function implementation.
bm_svr: Support Vector Regression models, from the kernlab package. See ksvm for a complete description and possible parametrization. See bm_svr for the function implementation.
bm_ffnn: Feedforward Neural Network models, from the nnet package. See nnet for a complete description and possible parametrization. See bm_ffnn for the function implementation.
bm_pls_pcr: Partial Least Regression and Principal Component Regression models, from the pls package. See mvr for a complete description and possible parametrization. See bm_pls_pcr for the function implementation.

learner_pars

a list with parameter setting for the learner. For each model, a inner list should be created with the specified parameters.

Check each implementation to see the possible variations of parameters (also examplified below).

# A PPR model and a GLM model with default parameters
model_specs(learner = c("bm_ppr", "bm_glm"), learner_pars = NULL)


# A PPR model and a SVR model. The listed parameters are combined
# with a cartesian product.
# With these specifications an ensemble with 6 predictive base
# models will be created. Two PPR models, one with 2 nterms
# and another with 4; and 4 SVR models, combining the kernel
# and C parameters.
specs <- model_specs(
 c("bm_ppr", "bm_svr"),
 list(bm_ppr = list(nterms = c(2, 4)),
      bm_svr = list(kernel = c("vanilladot", "polydot"), C = c(1,5)))
)

# All parameters currently available (parameter values can differ)
model_specs(
 learner = c("bm_ppr", "bm_svr", "bm_randomforest",
             "bm_gaussianprocess", "bm_cubist", "bm_glm",
             "bm_gbm", "bm_pls_pcr", "bm_ffnn", "bm_mars"
         ),
 learner_pars = list(
    bm_ppr = list(
       nterms = c(2,4),
       sm.method = "supsmu"
     ),
    bm_svr = list(
       kernel = "rbfdot",
       C = c(1,5),
       epsilon = .01
     ),
    bm_glm = list(
       alpha = c(1, 0)
     ),
    bm_randomforest = list(
       num.trees = 500
     ),
    bm_gbm = list(
       interaction.depth = 1,
       shrinkage = c(.01, .005),
       n.trees = c(100)
     ),
    bm_mars = list(
       nk = 15,
       degree = 3,
       thresh = .001
     ),
    bm_ffnn = list(
       size = 30,
       decay = .01
     ),
    bm_pls_pcr = list(
       method = c("kernelpls", "simpls", "cppls")
     ),
    bm_gaussianprocess = list(
       kernel = "vanilladot",
       tol = .01
     ),
    bm_cubist = list(
       committees = 50,
       neighbors = 0
     )
  )
)