3 #' Forecast encapsulation
6 #' @importFrom R6 R6Class
8 #' @field .pred List with \itemize{
9 #' \item serie: forecasted serie
10 #' \item params: corresponding list of parameters (weights, neighbors...)
11 #' \item index_in_data: corresponding index in data object}
12 #' @field .dates vector of day indices where forcast occurs
16 #' \item{\code{initialize(dates)}}{
17 #' Initialize a Forecast object with a series of date indices.}
18 #' \item{\code{getSize()}}{
19 #' Return number of individual forecasts.}
20 #' \item{\code{append(new_serie, new_params, new_index_in_data)}}{
21 #' Acquire a new individual forecast, with its (optimized) parameters and the corresponding
22 #' index in the dataset.}
23 #' \item{\code{getDates()}}{
24 #' Get dates where forecast occurs.}
25 #' \item{\code{getSerie(index)}}{
26 #' Get forecasted serie at specified index.}
27 #' \item{\code{getParams(index)}}{
28 #' Get parameters at specified index (for 'Neighbors' method).}
29 #' \item{\code{getIndexInData(index)}}{
30 #' Get index in data which corresponds to current forecast.}
32 Forecast = R6::R6Class("Forecast",
35 .dates = integer(0) #store dates as integers (from 1970-01-01)
38 initialize = function(dates)
40 private$.dates <- dates
46 append = function(new_serie, new_params, new_index_in_data)
48 private$.pred[[length(private$.pred)+1]] <-
49 list("serie"=new_serie, "params"=new_params, "index_in_data"=new_index_in_data)
52 as.Date( private$.dates, origin="1970-01-01" )
54 getSerie = function(index)
57 index = match(index, private$.dates)
58 private$.pred[[index]]$serie
60 getParams = function(index)
63 index = match(index, private$.dates)
64 private$.pred[[index]]$params
66 getIndexInData = function(index)
69 index = match(index, private$.dates)
70 private$.pred[[index]]$index_in_data