Fix time acquisition by adding 'tz' arg
[talweg.git] / pkg / R / F_Neighbors.R
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e030a6e3 1#' @include Forecaster.R
3d69ff21 2#'
25b75559 3#' Neighbors Forecaster
3d69ff21 4#'
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5#' Predict tomorrow as a weighted combination of "futures of the past" days.
6#' Inherits \code{\link{Forecaster}}
546b0cb6 7#'
25b75559 8NeighborsForecaster = R6::R6Class("NeighborsForecaster",
a66a84b5 9 inherit = Forecaster,
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10
11 public = list(
98e958ca 12 predictShape = function(data, today, memory, horizon, ...)
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13 {
14 # (re)initialize computed parameters
a66a84b5 15 private$.params <- list("weights"=NA, "indices"=NA, "window"=NA)
3d69ff21 16
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17 # Do not forecast on days with NAs (TODO: softer condition...)
18 if (any(is.na(data$getCenteredSerie(today))))
19 return (NA)
20
af3b84f4 21 # Determine indices of no-NAs days followed by no-NAs tomorrows
98e958ca 22 fdays = getNoNA2(data, max(today-memory,1), today-1)
af3b84f4 23
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24 # Get optional args
25 simtype = ifelse(hasArg("simtype"), list(...)$simtype, "mix") #or "endo", or "exo"
26 kernel = ifelse(hasArg("kernel"), list(...)$kernel, "Gauss") #or "Epan"
27 if (hasArg(h_window))
a66a84b5 28 {
98e958ca 29 return ( private$.predictShapeAux(data,
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30 fdays, today, horizon, list(...)$h_window, kernel, simtype, TRUE) )
31 }
3d69ff21 32
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33 # Indices of similar days for cross-validation; TODO: 45 = magic number
34 sdays = getSimilarDaysIndices(today, limit=45, same_season=FALSE)
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35
36 # Function to optimize h : h |--> sum of prediction errors on last 45 "similar" days
37 errorOnLastNdays = function(h, kernel, simtype)
38 {
39 error = 0
40 nb_jours = 0
f17665c7 41 for (i in intersect(fdays,sdays))
3d69ff21 42 {
f17665c7 43 # mix_strategy is never used here (simtype != "mix"), therefore left blank
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44 prediction = private$.predictShapeAux(data,
45 fdays, i, horizon, h, kernel, simtype, FALSE)
f17665c7 46 if (!is.na(prediction[1]))
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47 {
48 nb_jours = nb_jours + 1
af3b84f4 49 error = error +
98e958ca 50 mean((data$getCenteredSerie(i+1)[1:horizon] - prediction)^2)
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51 }
52 }
53 return (error / nb_jours)
54 }
55
f17665c7 56 if (simtype != "endo")
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57 {
58 h_best_exo = optimize(
59 errorOnLastNdays, c(0,10), kernel=kernel, simtype="exo")$minimum
60 }
3d69ff21 61 if (simtype != "exo")
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62 {
63 h_best_endo = optimize(
64 errorOnLastNdays, c(0,10), kernel=kernel, simtype="endo")$minimum
65 }
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66
67 if (simtype == "endo")
af3b84f4 68 {
98e958ca 69 return (private$.predictShapeAux(data,
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70 fdays, today, horizon, h_best_endo, kernel, "endo", TRUE))
71 }
3d69ff21 72 if (simtype == "exo")
af3b84f4 73 {
98e958ca 74 return (private$.predictShapeAux(data,
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75 fdays, today, horizon, h_best_exo, kernel, "exo", TRUE))
76 }
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77 if (simtype == "mix")
78 {
f17665c7 79 h_best_mix = c(h_best_endo,h_best_exo)
98e958ca 80 return(private$.predictShapeAux(data,
af3b84f4 81 fdays, today, horizon, h_best_mix, kernel, "mix", TRUE))
3d69ff21 82 }
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83 }
84 ),
85 private = list(
3d69ff21 86 # Precondition: "today" is full (no NAs)
98e958ca 87 .predictShapeAux = function(data, fdays, today, horizon, h, kernel, simtype, final_call)
3d69ff21 88 {
f17665c7 89 fdays = fdays[ fdays < today ]
3d69ff21 90 # TODO: 3 = magic number
f17665c7 91 if (length(fdays) < 3)
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92 return (NA)
93
94 if (simtype != "exo")
95 {
96 h_endo = ifelse(simtype=="mix", h[1], h)
97
98 # Distances from last observed day to days in the past
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99 distances2 = rep(NA, length(fdays))
100 for (i in seq_along(fdays))
3d69ff21 101 {
25b75559 102 delta = data$getCenteredSerie(today) - data$getCenteredSerie(fdays[i])
3d69ff21 103 # Require at least half of non-NA common values to compute the distance
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104 if ( !any( is.na(delta) ) )
105 distances2[i] = mean(delta^2)
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106 }
107
108 sd_dist = sd(distances2)
99f83c9a 109 if (sd_dist < .Machine$double.eps)
546b0cb6 110 {
fa5b7bfc 111# warning("All computed distances are very close: stdev too small")
99f83c9a 112 sd_dist = 1 #mostly for tests... FIXME:
546b0cb6 113 }
3d69ff21 114 simils_endo =
99f83c9a 115 if (kernel=="Gauss")
3d69ff21 116 exp(-distances2/(sd_dist*h_endo^2))
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117 else
118 {
119 # Epanechnikov
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120 u = 1 - distances2/(sd_dist*h_endo^2)
121 u[abs(u)>1] = 0.
122 u
123 }
124 }
125
126 if (simtype != "endo")
127 {
128 h_exo = ifelse(simtype=="mix", h[2], h)
129
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130 M = matrix( nrow=1+length(fdays), ncol=1+length(data$getExo(today)) )
131 M[1,] = c( data$getLevel(today), as.double(data$getExo(today)) )
f17665c7 132 for (i in seq_along(fdays))
25b75559 133 M[i+1,] = c( data$getLevel(fdays[i]), as.double(data$getExo(fdays[i])) )
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134
135 sigma = cov(M) #NOTE: robust covariance is way too slow
613a986f 136 sigma_inv = solve(sigma) #TODO: use pseudo-inverse if needed?
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137
138 # Distances from last observed day to days in the past
139 distances2 = rep(NA, nrow(M)-1)
140 for (i in 2:nrow(M))
141 {
142 delta = M[1,] - M[i,]
143 distances2[i-1] = delta %*% sigma_inv %*% delta
144 }
145
146 sd_dist = sd(distances2)
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147 if (sd_dist < .Machine$double.eps)
148 {
fa5b7bfc 149# warning("All computed distances are very close: stdev too small")
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150 sd_dist = 1 #mostly for tests... FIXME:
151 }
3d69ff21 152 simils_exo =
f17665c7 153 if (kernel=="Gauss")
3d69ff21 154 exp(-distances2/(sd_dist*h_exo^2))
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155 else
156 {
157 # Epanechnikov
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158 u = 1 - distances2/(sd_dist*h_exo^2)
159 u[abs(u)>1] = 0.
160 u
161 }
162 }
163
3d69ff21 164 similarities =
f17665c7 165 if (simtype == "exo")
3d69ff21 166 simils_exo
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167 else if (simtype == "endo")
168 simils_endo
169 else #mix
170 simils_endo * simils_exo
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171
172 prediction = rep(0, horizon)
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173 for (i in seq_along(fdays))
174 prediction = prediction + similarities[i] * data$getSerie(fdays[i]+1)[1:horizon]
3d69ff21 175 prediction = prediction / sum(similarities, na.rm=TRUE)
99f83c9a 176
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177 if (final_call)
178 {
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179 private$.params$weights <- similarities
180 private$.params$indices <- fdays
181 private$.params$window <-
546b0cb6 182 if (simtype=="endo")
3d69ff21 183 h_endo
546b0cb6 184 else if (simtype=="exo")
3d69ff21 185 h_exo
546b0cb6 186 else #mix
3d69ff21 187 c(h_endo,h_exo)
3d69ff21 188 }
99f83c9a 189
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190 return (prediction)
191 }
192 )
193)