3 #' Forecaster (abstract class, implemented by all forecasters)
6 #' @importFrom R6 R6Class
8 #' @field params List of computed parameters, for post-run analysis (dev)
9 #' @field data Dataset, object of class Data
10 #' @field pjump Function: how to predict the jump at day interface ?
12 #' @section Methods: \describe{
13 #' \item{\code{initialize(data, pjump)}}
14 #' {Initialize a Forecaster object with a Data object and a jump prediction function.}
15 #' \item{\code{predictSerie(today,memory,horizon,...)}}
16 #' {Predict a new serie of \code{horizon} values at day index \code{today}
17 #' using \code{memory} days in the past.}
18 #' \item{\code{predictShape(today,memory,horizon,...)}}
19 #' {Predict a new shape of \code{horizon} values at day index \code{today}
20 #' using \code{memory} days in the past.}
21 #' \item{\code{getParameters()}}
22 #' {Return (internal) parameters.}}
23 Forecaster = R6::R6Class("Forecaster",
30 initialize = function(data, pjump)
33 private$.pjump <- pjump
36 predictSerie = function(today, memory, horizon, ...)
38 # Parameters (potentially) computed during shape prediction stage
39 predicted_shape = self$predictShape(today, memory, horizon, ...)
40 predicted_delta = private$.pjump(
41 private$.data, today, memory, horizon, private$.params, ...)
42 # Predicted shape is aligned it on the end of current day + jump
43 predicted_shape + tail(private$.data$getSerie(today),1) -
44 predicted_shape[1] + predicted_delta
46 predictShape = function(today, memory, horizon, ...)
47 NULL #empty default implementation: to implement in inherited classes
49 getParameters = function()