ensemble_models: Ensemble Models

In microsoft/finnts: Microsoft Finance Time Series Forecasting Framework

Ensemble Models

Description

Create ensemble model forecasts

Usage

ensemble_models(
  run_info,
  parallel_processing = NULL,
  inner_parallel = FALSE,
  num_cores = NULL,
  seed = 123
)

Arguments

run_info	run info using the set_run_info() function
parallel_processing	Default of NULL runs no parallel processing and forecasts each individual time series one after another. 'local_machine' leverages all cores on current machine Finn is running on. 'spark' runs time series in parallel on a spark cluster in Azure Databricks or Azure Synapse.
inner_parallel	Run components of forecast process inside a specific time series in parallel. Can only be used if parallel_processing is set to NULL or 'spark'.
num_cores	Number of cores to run when parallel processing is set up. Used when running parallel computations on local machine or within Azure. Default of NULL uses total amount of cores on machine minus one. Can't be greater than number of cores on machine minus 1.
seed	Set seed for random number generator. Numeric value.

Value

Ensemble model outputs are written to disk

Examples

data_tbl <- timetk::m4_monthly %>%
  dplyr::rename(Date = date) %>%
  dplyr::mutate(id = as.character(id)) %>%
  dplyr::filter(
    Date >= "2013-01-01",
    Date <= "2015-06-01",
    id == "M750"
  )

run_info <- set_run_info()

prep_data(run_info,
  input_data = data_tbl,
  combo_variables = c("id"),
  target_variable = "value",
  date_type = "month",
  forecast_horizon = 3
)

prep_models(run_info,
  models_to_run = c("arima", "glmnet"),
  num_hyperparameters = 2
)

train_models(run_info,
  run_global_models = FALSE
)

ensemble_models(run_info)

microsoft/finnts documentation built on Oct. 30, 2024, 9:34 p.m.