some last fixes; results still not good...
[talweg.git] / pkg / R / computeForecast.R
CommitLineData
af3b84f4 1#' Compute forecast
3d69ff21 2#'
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3#' Predict time-series curves ("tomorrows") at the selected days indices ("todays").
4#' This function just runs a loop over all requested indices, and stores the individual
5#' forecasts into a list which is then turned into a Forecast object.
c4c329f6 6#'
102bcfda 7#' @param data Object of class Data, output of \code{getData()}.
2057c793 8#' @param indices Indices where to forecast (the day after); integers relative to the
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9#' beginning of data, or (convertible to) Date objects.
10#' @param forecaster Name of the main forecaster; more details: ?F_<forecastername>
3d69ff21 11#' \itemize{
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12#' \item Persistence : use last (similar, next) day
13#' \item Neighbors : weighted tomorrows of similar days
14#' \item Average : average tomorrow of all same day-in-week
e030a6e3 15#' \item Zero : just output 0 (benchmarking purpose)
3d69ff21 16#' }
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17#' @param pjump Function to predict the jump at the interface between two days;
18#' more details: ?J_<functionname>
3d69ff21 19#' \itemize{
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20#' \item Persistence : use last (similar, next) day
21#' \item Neighbors: re-use the weights from F_Neighbors
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22#' \item Zero: just output 0 (no adjustment)
23#' }
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24#' @param memory Data depth (in days) to be used for prediction.
25#' @param horizon Number of time steps to predict.
26#' @param ncores Number of cores for parallel execution (1 to disable).
27#' @param ... Additional parameters for the forecasting models.
3d69ff21 28#'
a66a84b5 29#' @return An object of class Forecast
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30#'
31#' @examples
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32#' ts_data <- system.file("extdata","pm10_mesures_H_loc.csv",package="talweg")
33#' exo_data <- system.file("extdata","meteo_extra_noNAs.csv",package="talweg")
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34#' data <- getData(ts_data, exo_data, input_tz="GMT", working_tz="GMT",
35#' predict_at=7, limit=200)
36#' pred <- computeForecast(data, 100:130, "Persistence", "Zero",
37#' memory=50, horizon=12, ncores=1)
3d69ff21 38#' \dontrun{#Sketch for real-time mode:
e169b5d5 39#' data <- Data$new()
e169b5d5 40#' forecaster <- MyForecaster$new(myJumpPredictFunc)
3d69ff21 41#' repeat {
e169b5d5 42#' # In the morning 7am+ or afternoon 1pm+:
c1be9898 43#' data$append(
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44#' times_from_H+1_yersteday_to_Hnow,
45#' PM10_values_of_last_24h,
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46#' exogenous_measures_of_last_24h,
47#' exogenous_predictions_for_next_24h)
102bcfda 48#' pred <- forecaster$predictSerie(data, data$getSize(), ...)
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49#' #do_something_with_pred
50#' }}
51#' @export
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52computeForecast = function(data, indices, forecaster, pjump, predict_from,
53 memory=Inf, horizon=length(data$getSerie(1)), ncores=3, ...)
3d69ff21 54{
e030a6e3 55 # (basic) Arguments sanity checks
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56 predict_from = as.integer(predict_from)[1]
57 if (! predict_from %in% 1:length(data$getSerie(1)))
58 stop("predict_from in [1,24] (hours)")
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59 if (hasArg("opera") && !list(...)$opera && memory < Inf)
60 memory <- Inf #finite memory in training mode makes no sense
3d69ff21 61 horizon = as.integer(horizon)[1]
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62 if (horizon<=predict_from || horizon>length(data$getSerie(1)))
63 stop("Horizon in [predict_from+1,24] (hours)")
98e958ca 64 integer_indices = sapply(indices, function(i) dateIndexToInteger(i,data))
a66a84b5 65 if (any(integer_indices<=0 | integer_indices>data$getSize()))
3d69ff21 66 stop("Indices out of range")
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67 if (!is.character(forecaster) || !is.character(pjump))
68 stop("forecaster (name) and pjump (function) should be of class character")
3d69ff21 69
98e958ca 70 pred = Forecast$new( sapply(indices, function(i) integerIndexToDate(i,data)) )
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71 forecaster_class_name = getFromNamespace(
72 paste(forecaster,"Forecaster",sep=""), "talweg")
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73 forecaster = forecaster_class_name$new( #.pjump =
74 getFromNamespace(paste("get",pjump,"JumpPredict",sep=""), "talweg"))
5e838b3e 75
1e8327df 76 computeOneForecast <- function(i)
a866acb3 77 {
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78 list(
79 "forecast" = forecaster$predictSerie(data,i,memory,predict_from,horizon,...),
80 "params" = forecaster$getParameters(),
81 "index" = i )
a866acb3 82 }
5e838b3e 83
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84 p <-
85 if (ncores > 1 && requireNamespace("parallel",quietly=TRUE))
86 parallel::mclapply(integer_indices, computeOneForecast, mc.cores=ncores)
87 else
88 lapply(integer_indices, computeOneForecast)
89
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90 # TODO: find a way to fill pred in //...
91 for (i in seq_along(integer_indices))
92 {
93 pred$append(
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94 forecast = p[[i]]$forecast,
95 params = p[[i]]$params,
96 index_in_data = p[[i]]$index
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97 )
98 }
25b75559 99 pred
3d69ff21 100}