From: Benjamin Auder Date: Thu, 16 Feb 2017 00:55:51 +0000 (+0100) Subject: new version, persistence -7 days X-Git-Url: https://git.auder.net/images/pieces/Cwda/doc/html/up.jpg?a=commitdiff_plain;h=e030a6e31232332b73187eda25870e843152c174;p=talweg.git new version, persistence -7 days --- diff --git a/DESCRIPTION b/DESCRIPTION index 82f3e84..f3ee58a 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,9 +1,10 @@ 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 [aut,cre], Jean-Michel Poggi [ctb], Bruno Portier , [ctb] @@ -13,22 +14,24 @@ Depends: 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' diff --git a/NOTES b/NOTES index 14a3132..8bab5f1 100644 --- a/NOTES +++ b/NOTES @@ -27,3 +27,6 @@ Sur exo on doit prédire moyenne du jour au lieu de courbe --------> analyser (? --> 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 diff --git a/R/D_Zero.R b/R/D_Zero.R deleted file mode 100644 index e9e5f6d..0000000 --- a/R/D_Zero.R +++ /dev/null @@ -1,9 +0,0 @@ -#' 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 -} diff --git a/R/Data.R b/R/Data.R index a17e262..9b7db3f 100644 --- a/R/Data.R +++ b/R/Data.R @@ -29,8 +29,21 @@ Data = setRefClass( }, 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)" diff --git a/R/S_Average.R b/R/F_Average.R similarity index 68% rename from R/S_Average.R rename to R/F_Average.R index 819531d..dab156a 100644 --- a/R/S_Average.R +++ b/R/F_Average.R @@ -1,19 +1,19 @@ -#' @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) diff --git a/R/F_Level.R b/R/F_Level.R new file mode 100644 index 0000000..e0132ae --- /dev/null +++ b/R/F_Level.R @@ -0,0 +1,52 @@ +#' @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$getSerie(index)[1:horizon] + index = index - 7 + }; + #TODO: next test is too strict + if (!any(is.na(last_similar_serie))) + { + if (all_memory) + { + sum_level = sum_level + mean(last_similar_serie) + nb_series = nb_series + 1 + } + else + return (rep(mean(last_similar_serie), horizon)) + }; + if (index < first_day) + { + if (all_memory) + return (rep(sum_level / nb_series, horizon)) + return (NA) + } + } + } + ) +) diff --git a/R/S_Neighbors.R b/R/F_Neighbors.R similarity index 88% rename from R/S_Neighbors.R rename to R/F_Neighbors.R index b8e32cc..f1aecb5 100644 --- a/R/S_Neighbors.R +++ b/R/F_Neighbors.R @@ -1,19 +1,19 @@ -#' @include ShapeForecaster.R +#' @include Forecaster.R #' -#' @title Neighbors Shape Forecaster +#' @title Neighbors Forecaster #' #' @description Predict tomorrow as a weighted combination of "futures of the past" days. -#' Inherits \code{\link{ShapeForecaster}} -NeighborsShapeForecaster = setRefClass( - Class = "NeighborsShapeForecaster", - contains = "ShapeForecaster", +#' Inherits \code{\link{Forecaster}} +NeighborsForecaster = setRefClass( + Class = "NeighborsForecaster", + contains = "Forecaster", methods = list( initialize = function(...) { callSuper(...) }, - predict = function(today, memory, horizon, ...) + predictShape = function(today, memory, horizon, ...) { # (re)initialize computed parameters params <<- list("weights"=NA, "indices"=NA, "window"=NA) @@ -67,8 +67,8 @@ NeighborsShapeForecaster = setRefClass( mix_strategy = ifelse(hasArg("mix_strategy"), list(...)$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(...)$h_window, kernel, + simtype, simthresh, mix_strategy, FALSE)) #END GET # Indices for cross-validation; TODO: 45 = magic number @@ -87,8 +87,8 @@ NeighborsShapeForecaster = setRefClass( { nb_jours = nb_jours + 1 # mix_strategy is never used here (simtype != "mix"), therefore left blank - prediction = .predictAux(fdays_indices, i, horizon, h, kernel, simtype, simthresh, - "", FALSE) + prediction = .predictShapeAux(fdays_indices, i, horizon, h, kernel, simtype, + simthresh, "", FALSE) if (!is.na(prediction[1])) error = error + mean((data$getCenteredSerie(i+1)[1:horizon] - prediction)^2) } @@ -110,22 +110,22 @@ NeighborsShapeForecaster = setRefClass( 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... diff --git a/R/S_Persistence.R b/R/F_Persistence.R similarity index 66% rename from R/S_Persistence.R rename to R/F_Persistence.R index 08647e3..f078484 100644 --- a/R/S_Persistence.R +++ b/R/F_Persistence.R @@ -1,19 +1,19 @@ -#' @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) diff --git a/R/F_Zero.R b/R/F_Zero.R new file mode 100644 index 0000000..247ab04 --- /dev/null +++ b/R/F_Zero.R @@ -0,0 +1,20 @@ +#' @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) + } + ) +) diff --git a/R/Forecaster.R b/R/Forecaster.R new file mode 100644 index 0000000..71cb667 --- /dev/null +++ b/R/Forecaster.R @@ -0,0 +1,48 @@ +#' @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 + } + ) +) diff --git a/R/D_Neighbors.R b/R/J_Neighbors.R similarity index 53% rename from R/D_Neighbors.R rename to R/J_Neighbors.R index dba5f37..03d3340 100644 --- a/R/D_Neighbors.R +++ b/R/J_Neighbors.R @@ -1,20 +1,19 @@ -#' 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 diff --git a/R/D_Persistence.R b/R/J_Persistence.R similarity index 75% rename from R/D_Persistence.R rename to R/J_Persistence.R index 979bf05..744b42a 100644 --- a/R/D_Persistence.R +++ b/R/J_Persistence.R @@ -1,8 +1,8 @@ -#' 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) diff --git a/R/J_Zero.R b/R/J_Zero.R new file mode 100644 index 0000000..c227609 --- /dev/null +++ b/R/J_Zero.R @@ -0,0 +1,9 @@ +#' 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 +} diff --git a/R/ShapeForecaster.R b/R/ShapeForecaster.R deleted file mode 100644 index 0b448d7..0000000 --- a/R/ShapeForecaster.R +++ /dev/null @@ -1,37 +0,0 @@ -#' @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 - } - ) -) - diff --git a/R/getError.R b/R/getError.R index 61e7910..affe6c3 100644 --- a/R/getError.R +++ b/R/getError.R @@ -9,7 +9,7 @@ #' @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) diff --git a/R/getForecast.R b/R/getForecast.R index e126946..fde8e45 100644 --- a/R/getForecast.R +++ b/R/getForecast.R @@ -1,44 +1,46 @@ #' @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$getCenteredSerie(2))) stop("Horizon too short or too long") @@ -46,30 +48,19 @@ getForecast = function(data, indices, memory, horizon, if (any(indices<=0 | indices>data$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) } diff --git a/R/plot.R b/R/plot.R index f551ef4..9a0dbcd 100644 --- a/R/plot.R +++ b/R/plot.R @@ -1,3 +1,29 @@ +#' @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) @@ -45,8 +71,8 @@ plotFilaments <- function(data, index, limit=60) 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( diff --git a/reports/report_2017-03-01.ipynb b/reports/report_2017-03-01.ipynb index 22e7dc4..3e58706 100644 --- a/reports/report_2017-03-01.ipynb +++ b/reports/report_2017-03-01.ipynb @@ -50,12 +50,11 @@ }, "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\")" ] }, { @@ -66,9 +65,9 @@ }, "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", @@ -176,12 +175,12 @@ }, "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\")" ] }, { @@ -192,9 +191,9 @@ }, "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", @@ -277,12 +276,12 @@ }, "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\")" ] }, { @@ -293,9 +292,9 @@ }, "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",