Package: talweg
Title: talweg : Time-series sAmpLes forecasted With ExoGenous variables
Version: 0.1-0
-Description: Forecast a curve sampled within the day (seconds, minutes, hours...),
- using past measured curves, history of some exogenous variables measurements
- and the exogenous prediction for tomorrow. Main method is getForecast()
+Description: Forecast a curve sampled within the day (seconds, minutes,
+ hours...), using past measured curves, history of some exogenous variables
+ measurements and the exogenous prediction for tomorrow. Main method is
+ getForecast()
Authors: Benjamin Auder <Benjamin.Auder@math.u-psud.fr> [aut,cre],
Jean-Michel Poggi <Jean-Michel.Poggi@parisdescartes.fr> [ctb],
Bruno Portier <Bruno.Portier@insa-rouen.fr>, [ctb]
Suggests:
roxygen2,
testthat,
- rmarkdown,
- rainbow
+ rmarkdown,
+ rainbow
LazyData: yes
URL: http://git.auder.net/?p=talweg.git
License: MIT + file LICENSE
RoxygenNote: 5.0.1
Collate:
- 'D_Neighbors.R'
- 'D_Persistence.R'
- 'D_Zero.R'
'Data.R'
+ 'Forecaster.R'
+ 'F_Average.R'
+ 'F_Level.R'
+ 'F_Neighbors.R'
+ 'F_Persistence.R'
+ 'F_Zero.R'
'Forecast.R'
- 'ShapeForecaster.R'
- 'S_Average.R'
- 'S_Neighbors.R'
- 'S_Persistence.R'
+ 'J_Neighbors.R'
+ 'J_Persistence.R'
+ 'J_Zero.R'
'getData.R'
'getError.R'
'getForecast.R'
--> javascript visualisation.................. + tard
--> soft predict: ne pas virer les séries à NA
+
+--> use R6 class: https://cran.r-project.org/web/packages/R6/vignettes/Introduction.html
+ https://cran.r-project.org/web/packages/R6/vignettes/Performance.html
+++ /dev/null
-#' Just predict zero deltas (for reference)
-#'
-#' @inheritParams getForecast
-#' @param today Index of the current day (predict tomorrow)
-#' @param shape_params Optional parameters returned by the shape forecaster
-getZeroDeltaForecast = function(data, today, memory, horizon, shape_params, ...)
-{
- 0
-}
},
getSize = function()
{
+ "Number of series in the dataset"
+
length(data)
},
+ getStdHorizon = function()
+ {
+ "'Standard' horizon, from t+1 to midnight"
+
+ L1 = length(data[[1]]$serie)
+ L2 = length(data[[2]]$serie)
+ if (L1 < L2)
+ L2 - L1
+ else
+ L1
+ },
append = function(new_time, new_serie, new_level, new_exo_hat, new_exo_Dm1)
{
"Acquire a new vector of lists (time, serie, level, exo_hat, exo_Dm1)"
-#' @include ShapeForecaster.R
+#' @include Forecaster.R
#'
-#' @title Average Shape Forecaster
+#' @title Average Forecaster
#'
#' @description Return the (pointwise) average of the all the (similar) centered day curves
-#' in the past. Inherits \code{\link{ShapeForecaster}}
-AverageShapeForecaster = setRefClass(
- Class = "AverageShapeForecaster",
- contains = "ShapeForecaster",
+#' in the past. Inherits \code{\link{Forecaster}}
+AverageForecaster = setRefClass(
+ Class = "AverageForecaster",
+ contains = "Forecaster",
methods = list(
initialize = function(...)
{
callSuper(...)
},
- predict = function(today, memory, horizon, ...)
+ predictShape = function(today, memory, horizon, ...)
{
avg = rep(0., horizon)
first_day = max(1, today-memory)
--- /dev/null
+#' @include Forecaster.R
+#'
+#' @title Level Forecaster
+#'
+#' @description Return flat serie of last observed level (on similar day).
+#' Inherits \code{\link{ShapeForecaster}}
+LevelForecaster = setRefClass(
+ Class = "LevelForecaster",
+ contains = "Forecaster",
+
+ methods = list(
+ initialize = function(...)
+ {
+ callSuper(...)
+ },
+ predict = function(today, memory, horizon, all_memory=TRUE, ...)
+ {
+ #return last (similar) day level, or on all memory if all_memory==TRUE
+ first_day = max(1, today-memory)
+ index = today-7 + 1
+ if (all_memory)
+ {
+ sum_level = 0.
+ nb_series = 0
+ }
+ repeat
+ {
+ {
+ last_similar_serie = data$getSerie(index)[1:horizon]
+ index = index - 7
+ };
+ #TODO: next test is too strict
+ if (!any(is.na(last_similar_serie)))
+ {
+ if (all_memory)
+ {
+ sum_level = sum_level + mean(last_similar_serie)
+ nb_series = nb_series + 1
+ }
+ else
+ return (rep(mean(last_similar_serie), horizon))
+ };
+ if (index < first_day)
+ {
+ if (all_memory)
+ return (rep(sum_level / nb_series, horizon))
+ return (NA)
+ }
+ }
+ }
+ )
+)
-#' @include ShapeForecaster.R
+#' @include Forecaster.R
#'
-#' @title Neighbors Shape Forecaster
+#' @title Neighbors Forecaster
#'
#' @description Predict tomorrow as a weighted combination of "futures of the past" days.
-#' Inherits \code{\link{ShapeForecaster}}
-NeighborsShapeForecaster = setRefClass(
- Class = "NeighborsShapeForecaster",
- contains = "ShapeForecaster",
+#' Inherits \code{\link{Forecaster}}
+NeighborsForecaster = setRefClass(
+ Class = "NeighborsForecaster",
+ contains = "Forecaster",
methods = list(
initialize = function(...)
{
callSuper(...)
},
- predict = function(today, memory, horizon, ...)
+ predictShape = function(today, memory, horizon, ...)
{
# (re)initialize computed parameters
params <<- list("weights"=NA, "indices"=NA, "window"=NA)
mix_strategy = ifelse(hasArg("mix_strategy"), list(...)$mix_strategy, "neighb") #or "mult"
same_season = ifelse(hasArg("same_season"), list(...)$same_season, TRUE)
if (hasArg(h_window))
- return (.predictAux(fdays_indices, today, horizon, list(...)$h_window, kernel, simtype,
- simthresh, mix_strategy, FALSE))
+ return (.predictShapeAux(fdays_indices, today, horizon, list(...)$h_window, kernel,
+ simtype, simthresh, mix_strategy, FALSE))
#END GET
# Indices for cross-validation; TODO: 45 = magic number
{
nb_jours = nb_jours + 1
# mix_strategy is never used here (simtype != "mix"), therefore left blank
- prediction = .predictAux(fdays_indices, i, horizon, h, kernel, simtype, simthresh,
- "", FALSE)
+ prediction = .predictShapeAux(fdays_indices, i, horizon, h, kernel, simtype,
+ simthresh, "", FALSE)
if (!is.na(prediction[1]))
error = error + mean((data$getCenteredSerie(i+1)[1:horizon] - prediction)^2)
}
if (simtype == "endo")
{
- return (.predictAux(fdays_indices, today, horizon, h_best_endo, kernel, "endo",
+ return (.predictShapeAux(fdays_indices, today, horizon, h_best_endo, kernel, "endo",
simthresh, "", TRUE))
}
if (simtype == "exo")
{
- return (.predictAux(fdays_indices, today, horizon, h_best_exo, kernel, "exo",
+ return (.predictShapeAux(fdays_indices, today, horizon, h_best_exo, kernel, "exo",
simthresh, "", TRUE))
}
if (simtype == "mix")
{
- return (.predictAux(fdays_indices, today, horizon, c(h_best_endo,h_best_exo), kernel,
- "mix", simthresh, mix_strategy, TRUE))
+ return (.predictShapeAux(fdays_indices, today, horizon, c(h_best_endo,h_best_exo),
+ kernel, "mix", simthresh, mix_strategy, TRUE))
}
},
# Precondition: "today" is full (no NAs)
- .predictAux = function(fdays_indices, today, horizon, h, kernel, simtype, simthresh,
+ .predictShapeAux = function(fdays_indices, today, horizon, h, kernel, simtype, simthresh,
mix_strategy, final_call)
{
dat = data$data #HACK: faster this way...
-#' @include ShapeForecaster.R
+#' @include Forecaster.R
#'
-#' @title Persistence Shape Forecaster
+#' @title Persistence Forecaster
#'
#' @description Return the last centered last (similar) day curve.
-#' Inherits \code{\link{ShapeForecaster}}
-PersistenceShapeForecaster = setRefClass(
- Class = "PersistenceShapeForecaster",
- contains = "ShapeForecaster",
+#' Inherits \code{\link{Forecaster}}
+PersistenceForecaster = setRefClass(
+ Class = "PersistenceForecaster",
+ contains = "Forecaster",
methods = list(
initialize = function(...)
{
callSuper(...)
},
- predict = function(today, memory, horizon, ...)
+ predictShape = function(today, memory, horizon, ...)
{
#return centered last (similar) day curve, avoiding NAs until memory is run
first_day = max(1, today-memory)
--- /dev/null
+#' @include Forecaster.R
+#'
+#' @title Zero Forecaster
+#'
+#' @description Return 0 (and then adjust). Inherits \code{\link{Forecaster}}
+ZeroForecaster = setRefClass(
+ Class = "ZeroForecaster",
+ contains = "Forecaster",
+
+ methods = list(
+ initialize = function(...)
+ {
+ callSuper(...)
+ },
+ predictShape = function(today, memory, horizon, ...)
+ {
+ rep(0., horizon)
+ }
+ )
+)
--- /dev/null
+#' @title Forecaster (abstract class)
+#'
+#' @description Abstract class to represent a forecaster (they all inherit this)
+#'
+#' @field params List of computed parameters, for post-run analysis (dev)
+#' @field data Dataset, object of class Data
+#' @field pjump Function: how to predict the jump at day interface ?
+Forecaster = setRefClass(
+ Class = "Forecaster",
+
+ fields = list(
+ params = "list",
+ data = "Data",
+ pjump = "function"
+ ),
+
+ methods = list(
+ initialize = function(...)
+ {
+ "Initialize (generic) Forecaster object"
+
+ callSuper(...)
+ if (!hasArg(data))
+ stop("Forecaster must be initialized with a Data object")
+ params <<- list()
+ },
+ predict = function(today, memory, horizon, ...)
+ {
+ "Obtain a new forecasted time-serie"
+
+ # Parameters (potentially) computed during shape prediction stage
+ predicted_shape = predictShape(today, memory, horizon, ...)
+ predicted_delta = pjump(data, today, memory, horizon, 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
+ },
+ predictShape = function(today, memory, horizon, ...)
+ {
+ "Shape prediction (centered curve)"
+
+ #empty default implementation: to implement in inherited classes
+ },
+ getParameters = function()
+ {
+ params
+ }
+ )
+)
-#' Obtain delta forecast by the Neighbors method
+#' Obtain jump forecast by the Neighbors method
#'
#' @inheritParams getForecast
-#' @inheritParams getZeroDeltaForecast
-getNeighborsDeltaForecast = function(data, today, memory, horizon, shape_params, ...)
+#' @inheritParams getZeroJumpPredict
+getNeighborsJumpPredict = function(data, today, memory, horizon, params, ...)
{
first_day = max(1, today-memory)
- filter = shape_params$indices >= first_day
- indices = shape_params$indices[filter]
- weights = shape_params$weights[filter]
+ filter = params$indices >= first_day
+ indices = params$indices[filter]
+ weights = params$weights[filter]
if (any(is.na(weights) | is.na(indices)))
return (NA)
gaps = sapply(indices, function(i) {
data$getSerie(i+1)[1] - tail(data$getSerie(i), 1)
})
-
scal_product = weights * gaps
norm_fact = sum( weights[!is.na(scal_product)] )
sum(scal_product, na.rm=TRUE) / norm_fact
-#' Obtain delta forecast by the Persistence method
+#' Obtain jump forecast by the Persistence method
#'
#' @inheritParams getForecast
-#' @inheritParams getZeroDeltaForecast
-getPersistenceDeltaForecast = function(data, today, memory, horizon, shape_params, ...)
+#' @inheritParams getZeroJumpPredict
+getPersistenceJumpPredict = function(data, today, memory, horizon, params, ...)
{
#return gap between end of similar day curve and first day of tomorrow (in the past)
first_day = max(1, today-memory)
--- /dev/null
+#' Just predict zero "jump" (for reference, benchmarking at least)
+#'
+#' @inheritParams getForecast
+#' @param today Index of the current day (predict tomorrow)
+#' @param params Optional parameters computed by the main forecaster
+getZeroJumpPredict = function(data, today, memory, horizon, params, ...)
+{
+ 0
+}
+++ /dev/null
-#' @title Shape Forecaster
-#'
-#' @description Generic class to represent a shape forecaster
-#'
-#' @field params List of computed parameters, potentially useful for some DeltaForecasters
-#' @field data Dataset, object of class Data
-ShapeForecaster = setRefClass(
- Class = "ShapeForecaster",
-
- fields = list(
- params = "list",
- data = "Data"
- ),
-
- methods = list(
- initialize = function(...)
- {
- "Initialize (generic) ShapeForecaster object"
-
- callSuper(...)
- if (!hasArg(data))
- stop("ShapeForecaster must be initialized with a Data object")
- params <<- list()
- },
- predict = function(today, memory, horizon, ...)
- {
- "Obtain a new forecasted time-serie (+ side-effect: compute parameters)"
-
- #empty default implementation: to implement in inherited classes
- },
- getParameters = function()
- {
- params
- }
- )
-)
-
#' @return A list (abs,MAPE) of lists (day,indices)
#'
#' @export
-getError = function(data, forecast, horizon)
+getError = function(data, forecast, horizon=data$getStdHorizon())
{
L = forecast$getSize()
mape_day = rep(0, horizon)
#' @title get Forecast
#'
#' @description Predict time-series curves for the selected days indices (lines in data).
-#' Run the forecasting task described by \code{delta_forecaster_name} and
-#' \code{shape_forecaster_name} on data obtained with \code{getData}
#'
#' @param data Dataset, object of type \code{Data} output of \code{getData}
#' @param indices Days indices where to forecast (the day after)
-#' @param memory Data depth (in days) to be used for prediction
-#' @param horizon Number of time steps to predict
-#' @param shape_forecaster_name Name of the shape forcaster
+#' @param forecaster Name of the main forcaster
#' \itemize{
#' \item Persistence : use values of last (similar, next) day
-#' \item Neighbors : use PM10 from the k closest neighbors' tomorrows
+#' \item Neighbors : use values from the k closest neighbors' tomorrows
#' \item Average : global average of all the (similar) "tomorrow of past"
+#' \item Zero : just output 0 (benchmarking purpose)
+#' \item Level : output a flat serie repeating the last observed level
#' }
-#' @param delta_forecaster_name Name of the delta forecaster
+#' @param pjump How to predict the jump at the interface between two days ?
#' \itemize{
#' \item Persistence : use last (similar) day values
-#' \item Neighbors: re-use the weights optimized in corresponding shape forecaster
+#' \item Neighbors: re-use the weights optimized in corresponding forecaster
#' \item Zero: just output 0 (no adjustment)
#' }
+#' @param memory Data depth (in days) to be used for prediction
+#' @param horizon Number of time steps to predict
#' @param ... Additional parameters for the forecasting models
#'
#' @return An object of class Forecast
#'
#' @examples
#' data = getData(ts_data="data/pm10_mesures_H_loc.csv", exo_data="data/meteo_extra_noNAs.csv",
-#' input_tz = "Europe/Paris", working_tz="Europe/Paris", predict_at="07")
-#' pred = getForecast(data, 2200:2230, Inf, 12, "Persistence", "Persistence")
+#' input_tz = "Europe/Paris", working_tz="Europe/Paris", predict_at=7)
+#' pred = getForecast(data, 2200:2230, "Persistence", "Persistence", 500, 12)
#' \dontrun{#Sketch for real-time mode:
#' data = new("Data", ...)
+#' forecaster = new(..., data=data)
#' repeat {
#' data$append(some_new_data)
-#' pred = getForecast(data, ...)
+#' pred = forecaster$predict(data$getSize(), ...)
#' #do_something_with_pred
#' }}
#' @export
-getForecast = function(data, indices, memory, horizon,
- shape_forecaster_name, delta_forecaster_name, ...)
+getForecast = function(data, indices, forecaster, pjump,
+ memory=Inf, horizon=data$getStdHorizon(), ...)
{
+ # (basic) Arguments sanity checks
horizon = as.integer(horizon)[1]
if (horizon<=0 || horizon>length(data$getCenteredSerie(2)))
stop("Horizon too short or too long")
if (any(indices<=0 | indices>data$getSize()))
stop("Indices out of range")
indices = sapply(indices, dateIndexToInteger, data)
-
- #NOTE: some assymetry here...
- shape_forecaster = new(paste(shape_forecaster_name,"ShapeForecaster",sep=""), data=data)
- #A little bit strange, but match.fun() and get() fail
- delta_forecaster = getFromNamespace(
- paste("get",delta_forecaster_name,"DeltaForecast",sep=""), "talweg")
+ if (!is.character(forecaster) || !is.character(pjump))
+ stop("forecaster and pjump should be of class character")
pred = list()
+ forecaster = new(paste(forecaster,"Forecaster",sep=""), data=data,
+ pjump = getFromNamespace(paste("get",pjump,"JumpPredict",sep=""), "talweg"))
for (today in indices)
{
- #shape always predicted first (on centered series, no scaling taken into account),
- #with side-effect: optimize some parameters (h, weights, ...)
- predicted_shape = shape_forecaster$predict(today, memory, horizon, ...)
- #then, delta prediction can re-use some variables optimized previously (like neighbors infos)
- predicted_delta = delta_forecaster(data, today, memory, horizon,
- shape_forecaster$getParameters(), ...)
-
- #TODO: this way is faster than a call to append(); why ?
pred[[length(pred)+1]] = list(
- # Predict shape and align it on end of current day
- serie = predicted_shape + tail( data$getSerie(today), 1 ) - predicted_shape[1] +
- predicted_delta, #add predicted jump
- params = shape_forecaster$getParameters(),
- index = today )
+ "serie" = forecaster$predict(today, memory, horizon, ...),
+ "params" = forecaster$getParameters(),
+ "index" = today
+ )
}
new("Forecast",pred=pred)
}
+#' @title plot curves
+#'
+#' @description Plot a range of curves in data
+#'
+#' @param data Object of class Data
+#' @param indices Range of indices (integers or dates)
+#'
+#' @export
+plotCurves <- function(data, indices)
+{
+ yrange = range( sapply( indices, function(i) {
+ serie = c(data$getCenteredSerie(i))
+ if (!all(is.na(serie)))
+ range(serie, na.rm=TRUE)
+ c()
+ }) )
+ par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
+ for (i in seq_along(indices))
+ {
+ plot(data$getSerie(indices[i]), type="l", ylim=yrange,
+ xlab=ifelse(i==1,"Temps (en heures)",""), ylab=ifelse(i==1,"PM10",""))
+ if (ii < length(indices))
+ par(new=TRUE)
+ }
+}
+
#' @title plot measured / predicted
#'
#' @description Plot measured curve (in black) and predicted curve (in red)
index = i - first_day + 1
serie = c(data$getCenteredSerie(index), data$getCenteredSerie(index+1))
if (!all(is.na(serie)))
- return ( range(serie, na.rm=TRUE) )
- return (0)
+ range(serie, na.rm=TRUE)
+ c()
}) )
grays = gray.colors(20, 0.1, 0.9) #TODO: 20 == magic number
colors = c(
},
"outputs": [],
"source": [
- "p_ch_nn = getForecast(data, seq(as.Date(\"2015-01-18\"),as.Date(\"2015-01-24\"),\"days\"), Inf, 17,\n",
- " \"Neighbors\", \"Neighbors\", simtype=\"mix\", same_season=FALSE, mix_strategy=\"mult\")\n",
- "p_ch_pz = getForecast(data, seq(as.Date(\"2015-01-18\"),as.Date(\"2015-01-24\"),\"days\"), Inf, 17,\n",
- " \"Persistence\", \"Zero\")\n",
- "p_ch_az = getForecast(data, seq(as.Date(\"2015-01-18\"),as.Date(\"2015-01-24\"),\"days\"), Inf, 17,\n",
- " \"Average\", \"Zero\")"
+ "indices = seq(as.Date(\"2015-01-18\"),as.Date(\"2015-01-24\"),\"days\")\n",
+ "p_ch_nn = getForecast(data,indices,\"Neighbors\",\"Neighbors\",simtype=\"mix\",same_season=FALSE,mix_strategy=\"mult\")\n",
+ "p_ch_pz = getForecast(data, indices, \"Persistence\", \"Zero\")\n",
+ "p_ch_az = getForecast(data, indices, \"Average\", \"Zero\")\n",
+ "p_ch_zz = getForecast(data, indices, \"Zero\", \"Zero\")"
]
},
{
},
"outputs": [],
"source": [
- "e_ch_nn = getError(data, p_ch_nn, 17)\n",
- "e_ch_pz = getError(data, p_ch_pz, 17)\n",
- "e_ch_az = getError(data, p_ch_az, 17)\n",
+ "e_ch_nn = getError(data, p_ch_nn)\n",
+ "e_ch_pz = getError(data, p_ch_pz)\n",
+ "e_ch_az = getError(data, p_ch_az)\n",
"options(repr.plot.width=9, repr.plot.height=6)\n",
"plotError(list(e_ch_nn, e_ch_pz, e_ch_az), cols=c(1,2,colors()[258]))\n",
"\n",
},
"outputs": [],
"source": [
- "p_ep_nn = getForecast(data, seq(as.Date(\"2015-03-15\"),as.Date(\"2015-03-21\"),\"days\"), Inf, 17,\n",
- " \"Neighbors\", \"Neighbors\", simtype=\"mix\", same_season=FALSE, mix_strategy=\"mult\")\n",
- "p_ep_pz = getForecast(data, seq(as.Date(\"2015-03-15\"),as.Date(\"2015-03-21\"),\"days\"), Inf, 17,\n",
- " \"Persistence\", \"Zero\")\n",
- "p_ep_az = getForecast(data, seq(as.Date(\"2015-03-15\"),as.Date(\"2015-03-21\"),\"days\"), Inf, 17,\n",
- " \"Average\", \"Zero\")"
+ "indices = seq(as.Date(\"2015-03-15\"),as.Date(\"2015-03-21\"),\"days\")\n",
+ "p_ep_nn = getForecast(data,indices,\"Neighbors\",\"Neighbors\",simtype=\"mix\",same_season=FALSE,mix_strategy=\"mult\")\n",
+ "p_ep_pz = getForecast(data, indices, \"Persistence\", \"Zero\")\n",
+ "p_ep_az = getForecast(data, indices, \"Average\", \"Zero\")\n",
+ "p_ep_zz = getForecast(data, indices, \"Zero\", \"Zero\")\n",
+ "p_ep_lz = getForecast(data, indices, \"Level\", \"Zero\")"
]
},
{
},
"outputs": [],
"source": [
- "e_ep_nn = getError(data, p_ep_nn, 17)\n",
- "e_ep_pz = getError(data, p_ep_pz, 17)\n",
- "e_ep_az = getError(data, p_ep_az, 17)\n",
+ "e_ep_nn = getError(data, p_ep_nn)\n",
+ "e_ep_pz = getError(data, p_ep_pz)\n",
+ "e_ep_az = getError(data, p_ep_az)\n",
"options(repr.plot.width=9, repr.plot.height=6)\n",
"plotError(list(e_ep_nn, e_ep_pz, e_ep_az), cols=c(1,2,colors()[258]))\n",
"\n",
},
"outputs": [],
"source": [
- "p_np_nn = getForecast(data, seq(as.Date(\"2015-04-26\"),as.Date(\"2015-05-02\"),\"days\"), Inf, 17,\n",
- " \"Neighbors\", \"Neighbors\", simtype=\"mix\", same_season=FALSE, mix_strategy=\"mult\")\n",
- "p_np_pz = getForecast(data, seq(as.Date(\"2015-04-26\"),as.Date(\"2015-05-02\"),\"days\"), Inf, 17,\n",
- " \"Persistence\", \"Zero\")\n",
- "p_np_az = getForecast(data, seq(as.Date(\"2015-04-26\"),as.Date(\"2015-05-02\"),\"days\"), Inf, 17,\n",
- " \"Average\", \"Zero\")"
+ "indices = seq(as.Date(\"2015-04-26\"),as.Date(\"2015-05-02\"),\"days\")\n",
+ "p_np_nn = getForecast(data,indices,\"Neighbors\",\"Neighbors\",simtype=\"mix\",same_season=FALSE,mix_strategy=\"mult\")\n",
+ "p_np_pz = getForecast(data, indices, \"Persistence\", \"Zero\")\n",
+ "p_np_az = getForecast(data, indices, \"Average\", \"Zero\")\n",
+ "p_np_zz = getForecast(data, indices, \"Zero\", \"Zero\")\n",
+ "p_np_lz = getForecast(data, indices, \"Level\", \"Zero\")"
]
},
{
},
"outputs": [],
"source": [
- "e_np_nn = getError(data, p_np_nn, 17)\n",
- "e_np_pz = getError(data, p_np_pz, 17)\n",
- "e_np_az = getError(data, p_np_az, 17)\n",
+ "e_np_nn = getError(data, p_np_nn)\n",
+ "e_np_pz = getError(data, p_np_pz)\n",
+ "e_np_az = getError(data, p_np_az)\n",
"options(repr.plot.width=9, repr.plot.height=6)\n",
"plotError(list(e_np_nn, e_np_pz, e_np_az), cols=c(1,2,colors()[258]))\n",
"\n",
projects / talweg.git / commitdiff