inherit = Forecaster,
public = list(
- predictSerie = function(data, today, memory, horizon, ...)
- {
- # Parameters (potentially) computed during shape prediction stage
- predicted_shape = self$predictShape(data, today, memory, horizon, ...)
-# predicted_delta = private$.pjump(data,today,memory,horizon,private$.params,...)
- # Predicted shape is aligned it on the end of current day + jump
-# predicted_shape+tail(data$getSerie(today),1)-predicted_shape[1]+predicted_delta
- predicted_shape
- },
+# predictSerie = function(data, today, memory, horizon, ...)
+# {
+# # Parameters (potentially) computed during shape prediction stage
+# predicted_shape = self$predictShape(data, today, memory, horizon, ...)
+## predicted_delta = private$.pjump(data,today,memory,horizon,private$.params,...)
+# # Predicted shape is aligned it on the end of current day + jump
+## predicted_shape+tail(data$getSerie(today),1)-predicted_shape[1]+predicted_delta
+# predicted_shape
+# },
predictShape = function(data, today, memory, horizon, ...)
{
# (re)initialize computed parameters
{
fdays = fdays[ fdays < today ]
# TODO: 3 = magic number
- if (length(fdays) < 3)
+ if (length(fdays) < 1)
return (NA)
# Neighbors: days in "same season"
levelToday = data$getLevel(today)
distances = sapply(indices, function(i) abs(data$getLevel(i)-levelToday))
same_pollution = (distances <= 2)
- if (sum(same_pollution) < 3) #TODO: 3 == magic number
+ if (sum(same_pollution) < 1) #TODO: 3 == magic number
{
same_pollution = (distances <= 5)
- if (sum(same_pollution) < 3)
+ if (sum(same_pollution) < 1)
return (NA)
}
indices = indices[same_pollution]
+ #TODO: we shouldn't need that block
+ if (length(indices) == 1)
+ {
+ if (final_call)
+ {
+ private$.params$weights <- 1
+ private$.params$indices <- indices
+ private$.params$window <- 1
+ }
+ return ( data$getSerie(indices[1])[1:horizon] ) #what else?!
+ }
+
if (simtype != "exo")
{
h_endo = ifelse(simtype=="mix", h[1], h)
sigma = cov(M) #NOTE: robust covariance is way too slow
# sigma_inv = solve(sigma) #TODO: use pseudo-inverse if needed?
sigma_inv = MASS::ginv(sigma)
-#if (final_call) browser()
+
# Distances from last observed day to days in the past
distances2 = sapply(seq_along(indices), function(i) {
delta = M[1,] - M[i+1,]
if (final_call)
{
private$.params$weights <- similarities
- private$.params$indices <- fdays
+ private$.params$indices <- indices
private$.params$window <-
if (simtype=="endo")
h_endo