R/getForecast.R

   1 #' @title get Forecast
   2 #'
   3 #' @description Predict time-series curves for the selected days indices (lines in data).
   4 #'   Run the forecasting task described by \code{delta_forecaster_name} and
   5 #'   \code{shape_forecaster_name} on data obtained with \code{getData}
   6 #'
   7 #' @param data Dataset, object of type \code{Data} output of \code{getData}
   8 #' @param indices Days indices where to forecast (the day after)
   9 #' @param memory Data depth (in days) to be used for prediction
  10 #' @param horizon Number of time steps to predict
  11 #' @param shape_forecaster_name Name of the shape forcaster
  12 #' \itemize{
  13 #'   \item Persistence : use values of last (similar, next) day
  14 #'   \item Neighbors : use PM10 from the k closest neighbors' tomorrows
  15 #'   \item Average : global average of all the (similar) "tomorrow of past"
  16 #' }
  17 #' @param delta_forecaster_name Name of the delta forecaster
  18 #' \itemize{
  19 #'   \item Persistence : use last (similar) day values
  20 #'   \item Neighbors: re-use the weights optimized in corresponding shape forecaster
  21 #'   \item Zero: just output 0 (no adjustment)
  22 #' }
  23 #' @param ... Additional parameters for the forecasting models
  24 #'
  25 #' @return An object of class Forecast
  26 #'
  27 #' @examples
  28 #' data = getData(ts_data="data/pm10_mesures_H_loc.csv", exo_data="data/meteo_extra_noNAs.csv",
  29 #'   input_tz = "Europe/Paris", working_tz="Europe/Paris", predict_at="07")
  30 #' pred = getForecast(data, 2200:2230, Inf, 12, "Persistence", "Persistence")
  31 #' \dontrun{#Sketch for real-time mode:
  32 #' data = new("Data", ...)
  33 #' repeat {
  34 #'   data$append(some_new_data)
  35 #'   pred = getForecast(data, ...)
  36 #'   #do_something_with_pred
  37 #' }}
  38 #' @export
  39 getForecast = function(data, indices, memory, horizon,
  40     shape_forecaster_name, delta_forecaster_name, ...)
  41 {
  42     horizon = as.integer(horizon)[1]
  43     if (horizon<=0 || horizon>length(data$getCenteredSerie(2)))
  44         stop("Horizon too short or too long")
  45     indices = as.integer(indices)
  46     if (any(indices<=0 | indices>data$getSize()))
  47         stop("Indices out of range")
  48     indices = sapply(indices, dateIndexToInteger, data)
  49
  50     #NOTE: some assymetry here...
  51     shape_forecaster = new(paste(shape_forecaster_name,"ShapeForecaster",sep=""), data=data)
  52     #A little bit strange, but match.fun() and get() fail
  53     delta_forecaster = getFromNamespace(
  54         paste("get",delta_forecaster_name,"DeltaForecast",sep=""), "talweg")
  55
  56     pred = list()
  57     for (today in indices)
  58     {
  59         #NOTE: To estimate timing...
  60 #       print(paste("Predict until index",today))
  61
  62         #shape always predicted first (on centered series, no scaling taken into account),
  63         #with side-effect: optimize some parameters (h, weights, ...)
  64         predicted_shape = shape_forecaster$predict(today, memory, horizon, ...)
  65         #then, delta prediction can re-use some variables optimized previously (like neighbors infos)
  66         predicted_delta = delta_forecaster(data, today, memory, horizon,
  67             shape_forecaster$getParameters(), ...)
  68
  69         #TODO: this way is faster than a call to append(); why ?
  70         pred[[length(pred)+1]] = list(
  71             # Predict shape and align it on end of current day
  72             serie = predicted_shape + tail( data$getSerie(today), 1 ) - predicted_shape[1],
  73                 predicted_delta, #add predicted jump
  74             params = shape_forecaster$getParameters(),
  75             index = today )
  76     }
  77     new("Forecast",pred=pred)
  78 }