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Time Series Model Tuner

Source: R/ts-model-tuner.R
ts_model_auto_tune.Rd

This function will create a tuned model. It uses the ts_model_spec_tune_template() under the hood to get the generic template that is used in the grid search.

Usage

ts_model_auto_tune(
  .modeltime_model_id,
  .calibration_tbl,
  .splits_obj,
  .drop_training_na = TRUE,
  .date_col,
  .value_col,
  .tscv_assess = "12 months",
  .tscv_skip = "6 months",
  .slice_limit = 6,
  .facet_ncol = 2,
  .grid_size = 30,
  .num_cores = 1,
  .best_metric = "rmse"
)

Arguments

.modeltime_model_id

The .model_id from a calibrated modeltime table.

.calibration_tbl

A calibrated modeltime table.

.splits_obj

The time_series_split object.

.drop_training_na

A boolean that will drop NA values from the training(splits) data

.date_col

The column that holds the date values.

.value_col

The column that holds the time series values.

.tscv_assess

A character expression like "12 months". This gets passed to timetk::time_series_cv()

.tscv_skip

A character expression like "6 months". This gets passed to timetk::time_series_cv()

.slice_limit

An integer that gets passed to timetk::time_series_cv()

.facet_ncol

The number of faceted columns to be passed to plot_time_series_cv_plan

.grid_size

An integer that gets passed to the dials::grid_latin_hypercube() function.

.num_cores

The default is 1, you can set this to any integer value as long as it is equal to or less than the available cores on your machine.

.best_metric

The default is "rmse" and this can be set to any default dials metric. This must be passed as a character.

Value

A list object with multiple items.

Details

This function can work with the following parsnip/modeltime engines:

  • "auto_arima"

  • "auto_arima_xgboost"

  • "ets"

  • "croston"

  • "theta"

  • "stlm_ets"

  • "tbats"

  • "stlm_arima"

  • "nnetar"

  • "prophet"

  • "prophet_xgboost"

  • "lm"

  • "glmnet"

  • "stan"

  • "spark"

  • "keras"

  • "earth"

  • "xgboost"

  • "kernlab"

This function returns a list object with several items inside of it. There are three categories of items that are inside of the list.

  • data

  • model_info

  • plots

The data section has the following items:

  • calibration_tbl This is the calibration data passed into the function.

  • calibration_tuned_tbl This is a calibration tibble that has used the tuned workflow.

  • tscv_data_tbl This is the tibble of the time series cross validation.

  • tuned_results This is a tuning results tibble with all slices from the time series cross validation.

  • best_tuned_results_tbl This is a tibble of the parameters for the best test set with the chosen metric.

  • tscv_obj This is the actual time series cross validation object returned from timetk::time_series_cv()

The model_info section has the following items:

  • model_spec This is the original modeltime/parsnip model specification.

  • model_spec_engine This is the engine used for the model specification.

  • model_spec_tuner This is the tuning model template returned from ts_model_spec_tune_template()

  • plucked_model This is the model that we have plucked from the calibration tibble for tuning.

  • wflw_tune_spec This is a new workflow with the model_spec_tuner attached.

  • grid_spec This is the grid search specification for the tuning process.

  • tuned_tscv_wflw_spec This is the final tuned model where the workflow and model have been finalized. This would be the model that you would want to pull out if you are going to work with it further.

The plots section has the following items:

  • tune_results_plt This is a static ggplot of the grid search.

  • tscv_pl This is the time series cross validation plan plot.

See also

Other Model Tuning: ts_model_spec_tune_template()

Other Utility: auto_stationarize(), calibrate_and_plot(), internal_ts_backward_event_tbl(), internal_ts_both_event_tbl(), internal_ts_forward_event_tbl(), model_extraction_helper(), ts_get_date_columns(), ts_info_tbl(), ts_is_date_class(), ts_lag_correlation(), ts_model_compare(), ts_model_rank_tbl(), ts_model_spec_tune_template(), ts_qq_plot(), ts_scedacity_scatter_plot(), ts_to_tbl(), util_difflog_ts(), util_doublediff_ts(), util_doubledifflog_ts(), util_log_ts(), util_singlediff_ts()

Author

Steven P. Sanderson II, MPH

Examples

if (FALSE) { # \dontrun{
suppressPackageStartupMessages(library(modeltime))
suppressPackageStartupMessages(library(timetk))
suppressPackageStartupMessages(library(dplyr))

data <- ts_to_tbl(AirPassengers) %>%
  select(-index)

splits <- time_series_split(
    data
    , date_col
    , assess = 12
    , skip = 3
    , cumulative = TRUE
)

rec_objs <- ts_auto_recipe(
  .data = data
  , .date_col = date_col
  , .pred_col = value
)

wfsets <- ts_wfs_mars(
  .model_type = "earth"
  , .recipe_list = rec_objs
)

wf_fits <- wfsets %>%
  modeltime_fit_workflowset(
    data = training(splits)
    , control = control_fit_workflowset(
     allow_par = TRUE
     , verbose = TRUE
    )
  )

models_tbl <- wf_fits %>%
  filter(.model != "NULL")

calibration_tbl <- models_tbl %>%
  modeltime_calibrate(new_data = testing(splits))

output <- ts_model_auto_tune(
  .modeltime_model_id = 1,
  .calibration_tbl = calibration_tbl,
  .splits_obj = splits,
  .drop_training_na = TRUE,
  .date_col = date_col,
  .value_col = value,
  .tscv_assess = "12 months",
  .tscv_skip = "3 months",
  .num_cores = parallel::detectCores() - 1
)
} # }