| 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 | # Indices of similar days for cross-validation; TODO: 45 = magic number |
| 23 | fdays = intersect( |
| 24 | getNoNA2(data, max(today-memory,1), today-1) |
| 25 | getSimilarDaysIndices(today, limit=45, same_season=TRUE) ) |
| 26 | |
| 27 | # Get optional args |
| 28 | kernel = ifelse(hasArg("kernel"), list(...)$kernel, "Gauss") #or "Epan" |
| 29 | if (hasArg(h_window)) |
| 30 | { |
| 31 | return ( private$.predictShapeAux(data, |
| 32 | fdays, today, horizon, list(...)$h_window, kernel, TRUE) ) |
| 33 | } |
| 34 | |
| 35 | # Function to optimize h : h |--> sum of prediction errors on last 45 "similar" days |
| 36 | errorOnLastNdays = function(h, kernel) |
| 37 | { |
| 38 | error = 0 |
| 39 | nb_jours = 0 |
| 40 | for (day in fdays) |
| 41 | { |
| 42 | # mix_strategy is never used here (simtype != "mix"), therefore left blank |
| 43 | prediction = private$.predictShapeAux(data,fdays,day,horizon,h,kernel,FALSE) |
| 44 | if (!is.na(prediction[1])) |
| 45 | { |
| 46 | nb_jours = nb_jours + 1 |
| 47 | error = error + |
| 48 | mean((data$getSerie(i+1)[1:horizon] - prediction)^2) |
| 49 | } |
| 50 | } |
| 51 | return (error / nb_jours) |
| 52 | } |
| 53 | |
| 54 | # h :: only for endo in this variation |
| 55 | h_best = optimize(errorOnLastNdays, c(0,10), kernel=kernel)$minimum |
| 56 | return (private$.predictShapeAux(data,fdays,today,horizon,h_best,kernel,TRUE)) |
| 57 | } |
| 58 | ), |
| 59 | private = list( |
| 60 | # Precondition: "today" is full (no NAs) |
| 61 | .predictShapeAux = function(data, fdays, today, horizon, h, kernel, final_call) |
| 62 | { |
| 63 | fdays = fdays[ fdays < today ] |
| 64 | # TODO: 3 = magic number |
| 65 | if (length(fdays) < 3) |
| 66 | return (NA) |
| 67 | |
| 68 | # ENDO:: Distances from last observed day to days in the past |
| 69 | distances2 = rep(NA, length(fdays)) |
| 70 | for (i in seq_along(fdays)) |
| 71 | { |
| 72 | delta = data$getSerie(today) - data$getSerie(fdays[i]) |
| 73 | # Require at least half of non-NA common values to compute the distance |
| 74 | if ( !any( is.na(delta) ) ) |
| 75 | distances2[i] = mean(delta^2) |
| 76 | } |
| 77 | |
| 78 | sd_dist = sd(distances2) |
| 79 | if (sd_dist < .Machine$double.eps) |
| 80 | { |
| 81 | # warning("All computed distances are very close: stdev too small") |
| 82 | sd_dist = 1 #mostly for tests... FIXME: |
| 83 | } |
| 84 | simils_endo = |
| 85 | if (kernel=="Gauss") |
| 86 | exp(-distances2/(sd_dist*h_endo^2)) |
| 87 | else |
| 88 | { |
| 89 | # Epanechnikov |
| 90 | u = 1 - distances2/(sd_dist*h_endo^2) |
| 91 | u[abs(u)>1] = 0. |
| 92 | u |
| 93 | } |
| 94 | |
| 95 | |
| 96 | # EXOGENS: distances computations are enough |
| 97 | # TODO: search among similar concentrations....... at this stage ?! |
| 98 | M = matrix( nrow=1+length(fdays), ncol=1+length(data$getExo(today)) ) |
| 99 | M[1,] = c( data$getLevel(today), as.double(data$getExo(today)) ) |
| 100 | for (i in seq_along(fdays)) |
| 101 | M[i+1,] = c( data$getLevel(fdays[i]), as.double(data$getExo(fdays[i])) ) |
| 102 | |
| 103 | sigma = cov(M) #NOTE: robust covariance is way too slow |
| 104 | sigma_inv = solve(sigma) #TODO: use pseudo-inverse if needed? |
| 105 | |
| 106 | # Distances from last observed day to days in the past |
| 107 | distances2 = rep(NA, nrow(M)-1) |
| 108 | for (i in 2:nrow(M)) |
| 109 | { |
| 110 | delta = M[1,] - M[i,] |
| 111 | distances2[i-1] = delta %*% sigma_inv %*% delta |
| 112 | } |
| 113 | |
| 114 | ppv <- sort(distances2, index.return=TRUE)$ix[1:10] #.............. |
| 115 | #PPV pour endo ? |
| 116 | |
| 117 | |
| 118 | similarities = |
| 119 | if (simtype == "exo") |
| 120 | simils_exo |
| 121 | else if (simtype == "endo") |
| 122 | simils_endo |
| 123 | else #mix |
| 124 | simils_endo * simils_exo |
| 125 | |
| 126 | prediction = rep(0, horizon) |
| 127 | for (i in seq_along(fdays)) |
| 128 | prediction = prediction + similarities[i] * data$getSerie(fdays[i]+1)[1:horizon] |
| 129 | prediction = prediction / sum(similarities, na.rm=TRUE) |
| 130 | |
| 131 | if (final_call) |
| 132 | { |
| 133 | private$.params$weights <- similarities |
| 134 | private$.params$indices <- fdays |
| 135 | private$.params$window <- h |
| 136 | } |
| 137 | |
| 138 | return (prediction) |
| 139 | } |
| 140 | ) |
| 141 | ) |