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[talweg.git] / pkg / R / Forecaster.R
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1#' Forecaster
2#'
3#' Forecaster (abstract class, implemented by all forecasters)
4#'
5#' @docType class
6#' @importFrom R6 R6Class
7#'
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 ?
11#'
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.}}
23Forecaster = R6::R6Class("Forecaster",
24 private = list(
25 .params = list(),
26 .data = NULL,
27 .pjump = NULL
28 ),
29 public = list(
30 initialize = function(data, pjump)
31 {
32 private$.data <- data
33 private$.pjump <- pjump
34 invisible(self)
35 },
36 predictSerie = function(today, memory, horizon, ...)
37 {
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
45 },
46 predictShape = function(today, memory, horizon, ...)
47 NULL #empty default implementation: to implement in inherited classes
48 ,
49 getParameters = function()
50 private$.params
51 )
52)