74 строки
1.9 KiB
R
74 строки
1.9 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/ensemble_models.R
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\name{ensemble_models}
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\alias{ensemble_models}
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\title{Ensemble Models}
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\usage{
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ensemble_models(
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run_info,
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parallel_processing = NULL,
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inner_parallel = FALSE,
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num_cores = NULL,
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seed = 123
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)
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}
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\arguments{
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\item{run_info}{run info using the \code{\link[=set_run_info]{set_run_info()}} function}
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\item{parallel_processing}{Default of NULL runs no parallel processing and
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forecasts each individual time series one after another. 'local_machine'
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leverages all cores on current machine Finn is running on. 'spark'
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runs time series in parallel on a spark cluster in Azure Databricks or
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Azure Synapse.}
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\item{inner_parallel}{Run components of forecast process inside a specific
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time series in parallel. Can only be used if parallel_processing is
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set to NULL or 'spark'.}
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\item{num_cores}{Number of cores to run when parallel processing is set up.
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Used when running parallel computations on local machine or within Azure.
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Default of NULL uses total amount of cores on machine minus one. Can't
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be greater than number of cores on machine minus 1.}
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\item{seed}{Set seed for random number generator. Numeric value.}
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}
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\value{
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Ensemble model outputs are written to disk
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}
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\description{
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Create ensemble model forecasts
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}
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\examples{
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\donttest{
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data_tbl <- timetk::m4_monthly \%>\%
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dplyr::rename(Date = date) \%>\%
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dplyr::mutate(id = as.character(id)) \%>\%
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dplyr::filter(
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Date >= "2013-01-01",
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Date <= "2015-06-01",
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id == "M750"
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)
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run_info <- set_run_info()
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prep_data(run_info,
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input_data = data_tbl,
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combo_variables = c("id"),
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target_variable = "value",
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date_type = "month",
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forecast_horizon = 3
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)
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prep_models(run_info,
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models_to_run = c("arima", "glmnet"),
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num_hyperparameters = 2
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)
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train_models(run_info,
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run_global_models = FALSE
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)
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ensemble_models(run_info)
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}
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}
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