forecastmodel.R

#' @export
forecastmodel <- R6::R6Class("forecastmodel", public = list(
    #----------------------------------------------------------------
    # Fields used for setting up the model
    # 
    # The expression (as character) used for generating the regprm
    regprmexpr = NA,
    # Regression parameters for the function used for fitting (rls, ls, etc.)
    regprm = list(), 
    # The off-line parameters
    prmbounds = as.matrix(data.frame(lower=NA, init=NA, upper=NA)),
    # List of inputs (which are R6 objects) (note the "cloning of list of reference objects" issue below in deep_clone function)
    inputs = list(),
    # Name of the output
    output = "y",
    # The range of the output to be used for cropping the output
    outputrange = NA,
    #----------------------------------------------------------------

    
    #----------------------------------------------------------------
    # Fields to be used when the model is fitted
    #
    # The horizons to fit for
    kseq = NA,
    # The (transformation stage) parameters (only the ones set in last call of insert_prm())
    prm = NA,
    # Stores the maximum lag for AR terms
    maxlagAR = NA,
    # Stores the maxlagAR past values of y for the update when new obs becomes available
    yAR = NA,
    # The fits, one for each k in kseq (simply a list with the latest fit)
    Lfits = list(),
    # Transformed input data (data.list with all inputs for regression)
    datatr = NA,
    #----------------------------------------------------------------

    
    #----------------------------------------------------------------
    # Contructor function
    initialize = function(){},
    #----------------------------------------------------------------

    
    #----------------------------------------------------------------    
    # Add inputs to the model
    add_inputs = function(...){
        dots <- list(...)
        for (i in 1:length(dots)){
            self$inputs[[ nams(dots)[i] ]] <- input_class$new(dots[[i]], model=self)
        }
    },
    #----------------------------------------------------------------

    #----------------------------------------------------------------
    # Add the expression (as character) which generates the regression parameters
    add_regprm = function(regprmexpr){
        self$regprmexpr <- regprmexpr
        self$regprm <- eval(parse(text = self$regprmexpr))
    },
    #----------------------------------------------------------------

    
    #----------------------------------------------------------------
    # Add the transformation parameters and bounds for optimization
    add_prmbounds = function(...) {
        dots <- list(...)
        for (i in 1:length(dots)) {
            nm <- names(dots)[i]
            if (nm %in% rownames(self$prmbounds)) {