train_models: Train Individual Models

In microsoft/finnts: Microsoft Finance Time Series Forecasting Framework

Train Individual Models

Description

Train Individual Models

Usage

train_models(
  run_info,
  run_global_models = FALSE,
  run_local_models = TRUE,
  global_model_recipes = c("R1"),
  feature_selection = FALSE,
  negative_forecast = FALSE,
  parallel_processing = NULL,
  inner_parallel = FALSE,
  num_cores = NULL,
  seed = 123
)

Arguments

run_info	run info using the set_run_info() function
run_global_models	If TRUE, run multivariate models on the entire data set (across all time series) as a global model. Can be override by models_not_to_run. Default of NULL runs global models for all date types except week and day.
run_local_models	If TRUE, run models by individual time series as local models.
global_model_recipes	Recipes to use in global models.
feature_selection	Implement feature selection before model training
negative_forecast	If TRUE, allow forecasts to dip below zero.
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

trained 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"
  )

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,
  back_test_scenarios = 6,
  run_ensemble_models = FALSE
)

train_models(run_info)

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