#' Grouped Time-Series Forecasting
#'
#' This learner supports prediction using grouped time-series modeling, using
#' \pkg{hts}. Fitting is done with \code{\link[hts]{hts}} and prediction is
#' performed via \code{\link[hts]{forecast.gts}}.
#'
#' @docType class
#'
#' @importFrom R6 R6Class
#' @importFrom assertthat assert_that is.count is.flag
#'
#' @export
#'
#' @keywords data
#'
#' @return Learner object with methods for training and prediction. See
#' \code{\link{Lrnr_base}} for documentation on learners.
#'
#' @format \code{\link{R6Class}} object.
#'
#' @family Learners
#'
#' @section Parameters:
#' \describe{
#' \item{\code{method)}}{Method for distributing forecasts within hierarchy.
#' See details of \code{\link[hts]{forecast.gts}}.}
#' \item{\code{weights)}}{Weights used for "optimal combination" method:
#' \code{weights="ols"} uses an unweighted combination (as described in
#' Hyndman et al 2011); \code{weights="wls"} uses weights based on forecast
#' variances (as described in Hyndman et al 2015); \code{weights="mint"}
#' uses a full covariance estimate to determine the weights (as described
#' in Hyndman et al 2016); \code{weights="nseries"} uses weights based on
#' the number of series aggregated at each node.}
#' \item{\code{fmethod)}}{Forecasting method to use for each series.}
#' \item{\code{algorithms)}}{An algorithm to be used for computing the
#' combination forecasts (when \code{method=="comb"}). The combination
#' forecasts are based on an ill-conditioned regression model. "lu"
#' indicates LU decomposition is used; "cg" indicates a conjugate gradient
#' method; "chol" corresponds to a Cholesky decomposition; "recursive"
#' indicates the recursive hierarchical algorithm of Hyndman et al (2015);
#' "slm" uses sparse linear regression. Note that \code{algorithms =
#' "recursive"} and \code{algorithms = "slm"} cannot be used if
#' \code{weights="mint"}.}
#' \item{\code{covariance)}}{Type of the covariance matrix to be used with
#' \code{weights="mint"}: either a shrinkage estimator ("shr") with
#' shrinkage towards the diagonal; or a sample covariance matrix ("sam").}
#' \item{\code{keep.fitted)}}{If \code{TRUE}, keep fitted values at the bottom
#' level.}
#' \item{\code{keep.resid)}}{If \code{TRUE}, keep residuals at the bottom
#' level.}
#' \item{\code{positive)}}{If \code{TRUE}, forecasts are forced to be strictly
#' positive (by setting \code{lambda=0}).}
#' \item{\code{lambda)}}{Box-Cox transformation parameter.}
#' \item{\code{level}}{Level used for "middle-out" method (only used when
#' \code{method = "mo"}).}
#' \item{\code{parallel}}{If \code{TRUE}, import \pkg{parallel} to allow
#' parallel processing.}
#' \item{\code{num.cores}}{If \code{parallel = TRUE}, specify how many cores
#' are going to be used.}
#' }
#'
#' @examples
#' # Example adapted from hts package manual
#' # Hierarchical structure looks like 2 child nodes associated with level 1,
#' # which are followed by 3 and 2 sub-child nodes respectively at level 2.
#' library(data.table)
#' library(hts)
#'
#' set.seed(3274)
#' abc <- as.data.table(5 + matrix(sort(rnorm(200)), ncol = 4, nrow = 50))
#' setnames(abc, paste("Series", 1:ncol(abc), sep = "_"))
#' abc[, time := .I]
#' grps <- rbind(c(1, 1, 2, 2), c(1, 2, 1, 2))
#' horizon <- 12
#' suppressWarnings(abc_long <- melt(abc, id = "time", variable.name = "series"))
#'
#' # create sl3 task (no outcome for hierarchical/grouped series)
#' node_list <- list(outcome = "value", time = "time", id = "series")
#' train_task <- sl3_Task$new(data = abc_long, nodes = node_list)
#' test_data <- expand.grid(time = 51:55, series = unique(abc_long$series))
#' test_data <- as.data.table(test_data)[, value := 0]
#' test_task <- sl3_Task$new(data = test_data, nodes = node_list)
#'
#' gts_learner <- Lrnr_gts$new()
#' gts_learner_fit <- gts_learner$train(train_task)
#' gts_learner_preds <- gts_learner_fit$predict(test_task)
Lrnr_gts <- R6Class(
classname = "Lrnr_gts",
inherit = Lrnr_base,
portable = TRUE,
class = TRUE,
public = list(
initialize = function(method = "comb",
weights = "wls",
fmethod = "ets",
algorithms = "lu",
covariance = "shr",
keep.fitted = FALSE,
keep.resid = FALSE,
positive = FALSE,
lambda = NULL,
level = NULL,
parallel = FALSE,
num.cores = 1,
...) {
params <- args_to_list()
super$initialize(params = params, ...)
}
),
private = list(
.properties = c("timeseries", "continuous"),
.train = function(task) {
args <- self$params
wide_formula <- sprintf("%s ~ %s", task$nodes$time, task$nodes$id)
args$y <- ts(as.matrix(dcast(task$data, as.formula(wide_formula),
value.var = task$nodes$outcome
))[, -1])
fit_object <- call_with_args(gts, args, silent = TRUE)
return(fit_object)
},
.predict = function(task = NULL) {
args <- self$params
# get horizon based on training and testing tasks
args$h <- ts_get_pred_horizon(self$training_task, task)
# get predictions for each time series
args$object <- private$.fit_object
gts_forecasts <- call_with_args(forecast.gts, args, silent = TRUE)$bts
# reformat predictions to match input task
times <- max(self$training_task$time) + seq_len(args$h)
gts_dt <-
as.data.table(gts_forecasts)[, time := times]
predictions <- melt(gts_dt, id.vars = "time", variable.name = "series")
test_data_formerge <- as.data.table(list(
time = task$get_node("time"),
series = task$get_node("id")
))
predictions <- merge(predictions, test_data_formerge, sort = FALSE)$value
return(predictions)
},
.required_packages = c("hts")
)
)
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