written my understanding of Neighbors2; TODO: sameSeasonSimilarIndices
[talweg.git] / pkg / R / computeForecast.R
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af3b84f4 1#' Compute forecast
3d69ff21 2#'
af3b84f4 3#' Predict time-series curves for the selected days indices (lines in data).
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4#'
5#' @param data Dataset, object of type \code{Data} output of \code{getData}
6#' @param indices Days indices where to forecast (the day after)
e030a6e3 7#' @param forecaster Name of the main forcaster
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8#' \itemize{
9#' \item Persistence : use values of last (similar, next) day
e030a6e3 10#' \item Neighbors : use values from the k closest neighbors' tomorrows
3d69ff21 11#' \item Average : global average of all the (similar) "tomorrow of past"
e030a6e3 12#' \item Zero : just output 0 (benchmarking purpose)
3d69ff21 13#' }
e030a6e3 14#' @param pjump How to predict the jump at the interface between two days ?
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15#' \itemize{
16#' \item Persistence : use last (similar) day values
e030a6e3 17#' \item Neighbors: re-use the weights optimized in corresponding forecaster
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18#' \item Zero: just output 0 (no adjustment)
19#' }
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20#' @param memory Data depth (in days) to be used for prediction
21#' @param horizon Number of time steps to predict
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22#' @param ... Additional parameters for the forecasting models
23#'
a66a84b5 24#' @return An object of class Forecast
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25#'
26#' @examples
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27#' ts_data = system.file("extdata","pm10_mesures_H_loc.csv",package="talweg")
28#' exo_data = system.file("extdata","meteo_extra_noNAs.csv",package="talweg")
29#' data = getData(ts_data, exo_data, input_tz = "Europe/Paris",
30#' working_tz="Europe/Paris", predict_at=7)
99f83c9a 31#' pred = computeForecast(data, 2200:2230, "Persistence", "Persistence", 500, 12)
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32#' \dontrun{#Sketch for real-time mode:
33#' data = new("Data", ...)
e030a6e3 34#' forecaster = new(..., data=data)
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35#' repeat {
36#' data$append(some_new_data)
e030a6e3 37#' pred = forecaster$predict(data$getSize(), ...)
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38#' #do_something_with_pred
39#' }}
40#' @export
25b75559 41computeForecast = function(data, indices, forecaster, pjump,
e030a6e3 42 memory=Inf, horizon=data$getStdHorizon(), ...)
3d69ff21 43{
e030a6e3 44 # (basic) Arguments sanity checks
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45 horizon = as.integer(horizon)[1]
46 if (horizon<=0 || horizon>length(data$getCenteredSerie(2)))
47 stop("Horizon too short or too long")
98e958ca 48 integer_indices = sapply(indices, function(i) dateIndexToInteger(i,data))
a66a84b5 49 if (any(integer_indices<=0 | integer_indices>data$getSize()))
3d69ff21 50 stop("Indices out of range")
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51 if (!is.character(forecaster) || !is.character(pjump))
52 stop("forecaster (name) and pjump (function) should be of class character")
3d69ff21 53
98e958ca 54 pred = Forecast$new( sapply(indices, function(i) integerIndexToDate(i,data)) )
25b75559 55 forecaster_class_name = getFromNamespace(paste(forecaster,"Forecaster",sep=""), "talweg")
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56 forecaster = forecaster_class_name$new( #.pjump =
57 getFromNamespace(paste("get",pjump,"JumpPredict",sep=""), "talweg"))
a66a84b5 58 for (today in integer_indices)
3d69ff21 59 {
25b75559 60 pred$append(
98e958ca 61 new_serie = forecaster$predictSerie(data, today, memory, horizon, ...),
25b75559 62 new_params = forecaster$getParameters(),
98e958ca 63 new_index_in_data = today
e030a6e3 64 )
3d69ff21 65 }
25b75559 66 pred
3d69ff21 67}