# (re)initialize computed parameters
params <<- list("weights"=NA, "indices"=NA, "window"=NA)
- first_day = max(today - memory, 1)
- # The first day is generally not complete:
- if (length(data$getCenteredSerie(1)) < length(data$getCenteredSerie(2)))
- first_day = 2
+ # Get optional args
+ simtype = ifelse(hasArg("simtype"), list(...)$simtype, "mix") #or "endo", or "exo"
+ kernel = ifelse(hasArg("kernel"), list(...)$kernel, "Gauss") #or "Epan"
+ if (hasArg(h_window))
+ return (.predictShapeAux(fdays,today,horizon,list(...)$h_window,kernel,simtype,TRUE))
- # Predict only on (almost) non-NAs days
+ # HACK for test reports: complete some days with a few NAs, for nicer graphics
nas_in_serie = is.na(data$getSerie(today))
if (any(nas_in_serie))
{
- #TODO: better define "repairing" conditions (and method)
if (sum(nas_in_serie) >= length(nas_in_serie) / 2)
return (NA)
for (i in seq_along(nas_in_serie))
}
# Determine indices of no-NAs days followed by no-NAs tomorrows
- fdays_indices = c()
- for (i in first_day:(today-1))
- {
- if ( !any(is.na(data$getSerie(i)) | is.na(data$getSerie(i+1))) )
- fdays_indices = c(fdays_indices, i)
- }
-
- #GET OPTIONAL PARAMS
- # Similarity computed with exogenous variables ? endogenous ? both ? ("exo","endo","mix")
- simtype = ifelse(hasArg("simtype"), list(...)$simtype, "mix")
- simthresh = ifelse(hasArg("simthresh"), list(...)$simthresh, 0.)
- kernel = ifelse(hasArg("kernel"), list(...)$kernel, "Gauss") #or "Epan"
- mix_strategy = ifelse(hasArg("mix_strategy"), list(...)$mix_strategy, "mult") #or "neighb"
- same_season = ifelse(hasArg("same_season"), list(...)$same_season, FALSE)
- if (hasArg(h_window))
- return (.predictShapeAux(fdays_indices, today, horizon, list(...)$h_window, kernel,
- simtype, simthresh, mix_strategy, TRUE))
- #END GET
+ first_day = max(today - memory, 1)
+ fdays = (first_day:(today-1))[ sapply(first_day:(today-1), function(i) {
+ !any(is.na(data$getSerie(i)) | is.na(data$getSerie(i+1)))
+ }) ]
- # Indices for cross-validation; TODO: 45 = magic number
- indices = getSimilarDaysIndices(today, limit=45, same_season=same_season)
- if (tail(indices,1) == 1)
- indices = head(indices,-1)
+ # Indices of similar days for cross-validation; TODO: 45 = magic number
+ sdays = getSimilarDaysIndices(today, limit=45, same_season=FALSE)
# Function to optimize h : h |--> sum of prediction errors on last 45 "similar" days
errorOnLastNdays = function(h, kernel, simtype)
{
error = 0
nb_jours = 0
- for (i in indices)
+ for (i in intersect(fdays,sdays))
{
- # NOTE: predict only on non-NAs days followed by non-NAs (TODO:)
- if (!any(is.na(data$getSerie(i)) | is.na(data$getSerie(i+1))))
+ # mix_strategy is never used here (simtype != "mix"), therefore left blank
+ prediction = .predictShapeAux(fdays, i, horizon, h, kernel, simtype, FALSE)
+ if (!is.na(prediction[1]))
{
nb_jours = nb_jours + 1
- # mix_strategy is never used here (simtype != "mix"), therefore left blank
- prediction = .predictShapeAux(fdays_indices, i, horizon, h, kernel, simtype,
- simthresh, "", FALSE)
- if (!is.na(prediction[1]))
- error = error + mean((data$getCenteredSerie(i+1)[1:horizon] - prediction)^2)
+ error = error + mean((data$getCenteredSerie(i+1)[1:horizon] - prediction)^2)
}
}
return (error / nb_jours)
}
- h_best_exo = 1.
- if (simtype != "endo" && !(simtype=="mix" && mix_strategy=="neighb"))
- {
- h_best_exo = optimize(errorOnLastNdays, interval=c(0,10), kernel=kernel,
- simtype="exo")$minimum
- }
+ if (simtype != "endo")
+ h_best_exo = optimize(errorOnLastNdays, c(0,10), kernel=kernel, simtype="exo")$minimum
if (simtype != "exo")
- {
- h_best_endo = optimize(errorOnLastNdays, interval=c(0,10), kernel=kernel,
- simtype="endo")$minimum
- }
+ h_best_endo = optimize(errorOnLastNdays, c(0,10), kernel=kernel, simtype="endo")$minimum
if (simtype == "endo")
- {
- return (.predictShapeAux(fdays_indices, today, horizon, h_best_endo, kernel, "endo",
- simthresh, "", TRUE))
- }
+ return (.predictShapeAux(fdays, today, horizon, h_best_endo, kernel, "endo", TRUE))
if (simtype == "exo")
- {
- return (.predictShapeAux(fdays_indices, today, horizon, h_best_exo, kernel, "exo",
- simthresh, "", TRUE))
- }
+ return (.predictShapeAux(fdays, today, horizon, h_best_exo, kernel, "exo", TRUE))
if (simtype == "mix")
{
- return (.predictShapeAux(fdays_indices, today, horizon, c(h_best_endo,h_best_exo),
- kernel, "mix", simthresh, mix_strategy, TRUE))
+ h_best_mix = c(h_best_endo,h_best_exo)
+ return (.predictShapeAux(fdays, today, horizon, h_best_mix, kernel, "mix", TRUE))
}
},
# Precondition: "today" is full (no NAs)
- .predictShapeAux = function(fdays_indices, today, horizon, h, kernel, simtype, simthresh,
- mix_strategy, final_call)
+ .predictShapeAux = function(fdays, today, horizon, h, kernel, simtype, final_call)
{
dat = data$data #HACK: faster this way...
- fdays_indices = fdays_indices[fdays_indices < today]
+ fdays = fdays[ fdays < today ]
# TODO: 3 = magic number
- if (length(fdays_indices) < 3)
+ if (length(fdays) < 3)
return (NA)
if (simtype != "exo")
h_endo = ifelse(simtype=="mix", h[1], h)
# Distances from last observed day to days in the past
- distances2 = rep(NA, length(fdays_indices))
- for (i in seq_along(fdays_indices))
+ distances2 = rep(NA, length(fdays))
+ for (i in seq_along(fdays))
{
- delta = dat[[today]]$serie - dat[[ fdays_indices[i] ]]$serie
+ delta = dat[[today]]$serie - dat[[ fdays[i] ]]$serie
# Require at least half of non-NA common values to compute the distance
if (sum(is.na(delta)) <= 0) #length(delta)/2)
distances2[i] = mean(delta^2) #, na.rm=TRUE)
{
h_exo = ifelse(simtype=="mix", h[2], h)
- M = matrix( nrow=1+length(fdays_indices), ncol=1+length(dat[[today]]$exo) )
+ M = matrix( nrow=1+length(fdays), ncol=1+length(dat[[today]]$exo) )
M[1,] = c( dat[[today]]$level, as.double(dat[[today]]$exo) )
- for (i in seq_along(fdays_indices))
- {
- M[i+1,] = c( dat[[ fdays_indices[i] ]]$level,
- as.double(dat[[ fdays_indices[i] ]]$exo) )
- }
+ for (i in seq_along(fdays))
+ M[i+1,] = c( dat[[ fdays[i] ]]$level, as.double(dat[[ fdays[i] ]]$exo) )
sigma = cov(M) #NOTE: robust covariance is way too slow
sigma_inv = solve(sigma) #TODO: use pseudo-inverse if needed?
sd_dist = sd(distances2)
simils_exo =
- if (kernel=="Gauss") {
+ if (kernel=="Gauss")
exp(-distances2/(sd_dist*h_exo^2))
- } else { #Epanechnikov
+ else { #Epanechnikov
u = 1 - distances2/(sd_dist*h_exo^2)
u[abs(u)>1] = 0.
u
}
}
- if (simtype=="mix")
- {
- if (mix_strategy == "neighb")
- {
- #Only (60) most similar days according to exogen variables are kept into consideration
- #TODO: 60 = magic number
- keep_indices = sort(simils_exo, index.return=TRUE)$ix[1:(min(60,length(simils_exo)))]
- simils_endo[-keep_indices] = 0.
- }
- else #mix_strategy == "mult"
- simils_endo = simils_endo * simils_exo
- }
-
similarities =
- if (simtype != "exo") {
- simils_endo
- } else {
+ if (simtype == "exo")
simils_exo
- }
-
- if (simthresh > 0.)
- {
- max_sim = max(similarities)
- # Set to 0 all similarities s where s / max_sim < simthresh, but keep at least 60
- ordering = sort(similarities / max_sim, index.return=TRUE)
- if (ordering[60] < simthresh)
- {
- similarities[ ordering$ix[ - (1:60) ] ] = 0.
- } else
- {
- limit = 61
- while (limit < length(similarities) && ordering[limit] >= simthresh)
- limit = limit + 1
- similarities[ ordering$ix[ - 1:limit] ] = 0.
- }
- }
+ else if (simtype == "endo")
+ simils_endo
+ else #mix
+ simils_endo * simils_exo
prediction = rep(0, horizon)
for (i in seq_along(fdays_indices))
h_endo
} else if (simtype=="exo") {
h_exo
- } else {
+ } else { #mix
c(h_endo,h_exo)
}
}
projects / talweg.git / blobdiff