| 1 | #' Forecaster |
| 2 | #' |
| 3 | #' Forecaster (abstract class, implemented by all forecasters). |
| 4 | #' |
| 5 | #' A Forecaster object encapsulates parameters (which can be of various kinds, for |
| 6 | #' example "Neighbors" method stores informations about the considered neighborhood for |
| 7 | #' the current prediction task) and one main function: \code{predictSerie()}. This last |
| 8 | #' function (by default) calls \code{predictShape()} to get a forecast of a centered |
| 9 | #' serie, and then calls the "jump prediction" function if it's provided -- see "field" |
| 10 | #' section -- to adjust it based on the last observed values. The main method in derived |
| 11 | #' forecasters is \code{predictShape()}; see 'Methods' section. |
| 12 | #' |
| 13 | #' @usage # Forecaster$new(pjump) #warning: predictShape() is unimplemented |
| 14 | #' |
| 15 | #' @field .params List of computed parameters (if applicable). |
| 16 | #' @field .pjump Function: how to predict the jump at day interface? The arguments of |
| 17 | #' this function are -- in this order: |
| 18 | #' \itemize{ |
| 19 | #' \item data: object output of \code{getData()}, |
| 20 | #' \item today: index of the current day in data (known until predict_from-1), |
| 21 | #' \item memory: number of days to use in the past (including today), |
| 22 | #' \item predict_from: first time step to predict (in [1,24]) |
| 23 | #' \item horizon: last time step to predict (in [predict_from,24]), |
| 24 | #' \item params: optimized parameters in the main method \code{predictShape()}, |
| 25 | #' \item ...: additional arguments. |
| 26 | #' } |
| 27 | #' .pjump returns an estimation of the jump after the last observed value. |
| 28 | #' |
| 29 | #' @section Methods: |
| 30 | #' \describe{ |
| 31 | #' \item{\code{initialize(data, pjump)}}{ |
| 32 | #' Initialize a Forecaster object with a Data object and a jump prediction function, |
| 33 | #' or NULL if \code{predictShape()} returns an adjusted curve.} |
| 34 | #' \item{\code{predictSerie(data,today,memory,predict_from,horizon,...)}}{ |
| 35 | #' Predict the next curve (at index today) from predict_from to horizon (hours), using |
| 36 | #' \code{memory} days in the past.} |
| 37 | #' \item{\code{predictShape(data,today,memory,predict_from,horizon,...)}}{ |
| 38 | #' Predict the shape of the next curve (at index today) from predict_from to horizon |
| 39 | #' (hours), using \code{memory} days in the past.} |
| 40 | #' \item{\code{getParameters()}}{ |
| 41 | #' Return (internal) parameters.} |
| 42 | #' } |
| 43 | #' |
| 44 | #' @docType class |
| 45 | #' @format R6 class |
| 46 | #' |
| 47 | Forecaster = R6::R6Class("Forecaster", |
| 48 | private = list( |
| 49 | .params = list(), |
| 50 | .pjump = NULL |
| 51 | ), |
| 52 | public = list( |
| 53 | initialize = function(pjump) |
| 54 | { |
| 55 | private$.pjump <- pjump |
| 56 | invisible(self) |
| 57 | }, |
| 58 | predictSerie = function(data, today, memory, predict_from, horizon, ...) |
| 59 | { |
| 60 | # Parameters (potentially) computed during shape prediction stage |
| 61 | predicted_shape <- self$predictShape(data,today,memory,predict_from,horizon,...) |
| 62 | predicted_delta <- |
| 63 | if (is.null(private$.pjump)) |
| 64 | NULL |
| 65 | else |
| 66 | private$.pjump(data,today,memory,predict_from,horizon,private$.params,...) |
| 67 | |
| 68 | # Predicted shape is aligned on the end of current day + jump (if jump!=NULL) |
| 69 | c( data$getSerie(today)[if (predict_from>=2) 1:(predict_from-1) else c()], |
| 70 | predicted_shape + ifelse( is.null(private$.pjump), |
| 71 | 0, |
| 72 | predicted_delta - predicted_shape[1] + |
| 73 | ifelse(predict_from>=2, |
| 74 | data$getSerie(today)[predict_from-1], tail(data$getSerie(today-1),1)) ) ) |
| 75 | }, |
| 76 | predictShape = function(data, today, memory, predict_from, horizon, ...) |
| 77 | NULL #empty default implementation: to implement in inherited classes |
| 78 | , |
| 79 | getParameters = function() |
| 80 | private$.params |
| 81 | ) |
| 82 | ) |