pkg/R/F_Neighbors2.R

   1 #' @include Forecaster.R
   2 #'
   3 #' Neighbors2 Forecaster
   4 #'
   5 #' Predict tomorrow as a weighted combination of "futures of the past" days.
   6 #' Inherits \code{\link{Forecaster}}
   7 #'
   8 Neighbors2Forecaster = R6::R6Class("Neighbors2Forecaster",
   9     inherit = Forecaster,
  10
  11     public = list(
  12         predictShape = function(data, today, memory, horizon, ...)
  13         {
  14             # (re)initialize computed parameters
  15             private$.params <- list("weights"=NA, "indices"=NA, "window"=NA)
  16
  17             # Do not forecast on days with NAs (TODO: softer condition...)
  18             if (any(is.na(data$getCenteredSerie(today))))
  19                 return (NA)
  20
  21             # Determine indices of no-NAs days followed by no-NAs tomorrows
  22             fdays = getNoNA2(data, max(today-memory,1), today-1)
  23
  24             # Get optional args
  25             kernel = ifelse(hasArg("kernel"), list(...)$kernel, "Gauss") #or "Epan"
  26             if (hasArg(h_window))
  27             {
  28                 return ( private$.predictShapeAux(data,
  29                     fdays, today, horizon, list(...)$h_window, kernel, TRUE) )
  30             }
  31
  32
  33             # Indices of similar days for cross-validation; TODO: 45 = magic number
  34             # TODO: ici faut une sorte de "same_season==TRUE" --> mois similaires epandage
  35             sdays = getSimilarDaysIndices(today, limit=45, same_season=FALSE)
  36
  37
  38             # Function to optimize h : h |--> sum of prediction errors on last 45 "similar" days
  39             errorOnLastNdays = function(h, kernel)
  40             {
  41                 error = 0
  42                 nb_jours = 0
  43                 for (i in intersect(fdays,sdays))
  44                 {
  45                     # mix_strategy is never used here (simtype != "mix"), therefore left blank
  46                     prediction = private$.predictShapeAux(data, fdays, i, horizon, h, kernel, FALSE)
  47                     if (!is.na(prediction[1]))
  48                     {
  49                         nb_jours = nb_jours + 1
  50                         error = error +
  51                             mean((data$getSerie(i+1)[1:horizon] - prediction)^2)
  52                     }
  53                 }
  54                 return (error / nb_jours)
  55             }
  56
  57             # h :: only for endo in this variation
  58             h_best_endo = optimize(errorOnLastNdays, c(0,10), kernel=kernel)$minimum
  59
  60             return (private$.predictShapeAux(data, fdays, today, horizon, h_best, kernel, TRUE))
  61         }
  62     ),
  63     private = list(
  64         # Precondition: "today" is full (no NAs)
  65         .predictShapeAux = function(data, fdays, today, horizon, h, kernel, final_call)
  66         {
  67             fdays = fdays[ fdays < today ]
  68             # TODO: 3 = magic number
  69             if (length(fdays) < 3)
  70                 return (NA)
  71
  72             # ENDO:: Distances from last observed day to days in the past
  73             distances2 = rep(NA, length(fdays))
  74             for (i in seq_along(fdays))
  75             {
  76                 delta = data$getSerie(today) - data$getSerie(fdays[i])
  77                 # Require at least half of non-NA common values to compute the distance
  78                 if ( !any( is.na(delta) ) )
  79                     distances2[i] = mean(delta^2)
  80             }
  81
  82             sd_dist = sd(distances2)
  83             if (sd_dist < .Machine$double.eps)
  84             {
  85 #                   warning("All computed distances are very close: stdev too small")
  86                 sd_dist = 1 #mostly for tests... FIXME:
  87             }
  88             simils_endo =
  89                 if (kernel=="Gauss")
  90                     exp(-distances2/(sd_dist*h_endo^2))
  91                 else
  92                 {
  93                     # Epanechnikov
  94                     u = 1 - distances2/(sd_dist*h_endo^2)
  95                     u[abs(u)>1] = 0.
  96                     u
  97                 }
  98
  99             # EXOGENS: distances computations are enough
 100             # TODO: search among similar concentrations....... at this stage ?!
 101             M = matrix( nrow=1+length(fdays), ncol=1+length(data$getExo(today)) )
 102             M[1,] = c( data$getLevel(today), as.double(data$getExo(today)) )
 103             for (i in seq_along(fdays))
 104                 M[i+1,] = c( data$getLevel(fdays[i]), as.double(data$getExo(fdays[i])) )
 105
 106             sigma = cov(M) #NOTE: robust covariance is way too slow
 107             sigma_inv = solve(sigma) #TODO: use pseudo-inverse if needed?
 108
 109             # Distances from last observed day to days in the past
 110             distances2 = rep(NA, nrow(M)-1)
 111             for (i in 2:nrow(M))
 112             {
 113                 delta = M[1,] - M[i,]
 114                 distances2[i-1] = delta %*% sigma_inv %*% delta
 115             }
 116
 117             ppv <- sort(distances2, index.return=TRUE)$ix[1:10] #..............
 118 #PPV pour endo ?
 119
 120             similarities =
 121                 if (simtype == "exo")
 122                     simils_exo
 123                 else if (simtype == "endo")
 124                     simils_endo
 125                 else #mix
 126                     simils_endo * simils_exo
 127
 128             prediction = rep(0, horizon)
 129             for (i in seq_along(fdays))
 130                 prediction = prediction + similarities[i] * data$getSerie(fdays[i]+1)[1:horizon]
 131             prediction = prediction / sum(similarities, na.rm=TRUE)
 132
 133             if (final_call)
 134             {
 135                 private$.params$weights <- similarities
 136                 private$.params$indices <- fdays
 137                 private$.params$window <-
 138                     if (simtype=="endo")
 139                         h_endo
 140                     else if (simtype=="exo")
 141                         h_exo
 142                     else #mix
 143                         c(h_endo,h_exo)
 144             }
 145
 146             return (prediction)
 147         }
 148     )
 149 )