Modeltime Workflow

The main workflow functions for time series modeling.

Core Functions

modeltime_table() as_modeltime_table()

Scale forecast analysis with a Modeltime Table

modeltime_calibrate()

Preparation for forecasting

modeltime_forecast()

Forecast future data

modeltime_accuracy()

Calculate Accuracy Metrics

modeltime_refit()

Refit one or more trained models to new data

modeltime_fit_workflowset()

Fit a workflowset object to one or multiple time series

Recursive Forecast Prediction

recursive()

Create a Recursive Time Series Model from a Parsnip or Workflow Regression Model

panel_tail()

Filter the last N rows (Tail) for multiple time series

Plotting & Tables

plot_modeltime_forecast()

Interactive Forecast Visualization

plot_modeltime_residuals()

Interactive Residuals Visualization

table_modeltime_accuracy()

Interactive Accuracy Tables

Residual Analysis

modeltime_residuals()

Extract Residuals Information

modeltime_residuals_test()

Apply Statistical Tests to Residuals

plot_modeltime_residuals()

Interactive Residuals Visualization

Nested Forecasting

Forecast many time series iteratively using “nested modeltime tables”. Used to apply models to each time series panel independently.

Core functions

modeltime_nested_fit()

Fit Tidymodels Workflows to Nested Time Series

modeltime_nested_select_best()

Select the Best Models from Nested Modeltime Table

modeltime_nested_refit()

Refits a Nested Modeltime Table

modeltime_nested_forecast()

Modeltime Nested Forecast

Extractors

extract_nested_test_accuracy() extract_nested_test_forecast() extract_nested_error_report() extract_nested_best_model_report() extract_nested_future_forecast() extract_nested_modeltime_table() extract_nested_train_split() extract_nested_test_split()

Log Extractor Functions for Modeltime Nested Tables

Workflow

extend_timeseries() nest_timeseries() split_nested_timeseries()

Prepared Nested Modeltime Data

Algorithms

The parsnip-adjacent algorithms that implement time series models.

Core Forecasting Methods

These models come with modeltime.

prophet_reg()

General Interface for PROPHET Time Series Models

prophet_boost()

General Interface for Boosted PROPHET Time Series Models

arima_reg()

General Interface for ARIMA Regression Models

arima_boost()

General Interface for "Boosted" ARIMA Regression Models

exp_smoothing()

General Interface for Exponential Smoothing State Space Models

seasonal_reg()

General Interface for Multiple Seasonality Regression Models (TBATS, STLM)

nnetar_reg()

General Interface for NNETAR Regression Models

Additional Algorithms

These algorithms have additional dependencies that can be installed with dependencies = TRUE

adam_reg()

General Interface for ADAM Regression Models

temporal_hierarchy()

General Interface for Temporal Hierarchical Forecasting (THIEF) Models

Baseline Algorithms (Simple Methods)

window_reg()

General Interface for Window Forecast Models

naive_reg()

General Interface for NAIVE Forecast Models

Parallel Processing

parallel_start() parallel_stop()

Start parallel clusters using parallel package

control_refit() control_fit_workflowset() control_nested_fit() control_nested_refit() control_nested_forecast()

Control aspects of the training process

create_model_grid()

Helper to make parsnip model specs from a dials parameter grid

Modeltime Workflow Helpers

combine_modeltime_tables()

Combine multiple Modeltime Tables into a single Modeltime Table

add_modeltime_model()

Add a Model into a Modeltime Table

update_modeltime_model()

Update the model by model id in a Modeltime Table

update_model_description() update_modeltime_description()

Update the model description by model id in a Modeltime Table

pluck_modeltime_model() pull_modeltime_model()

Extract model by model id in a Modeltime Table

Accuracy Metrics (Yardstick)

Metric Sets and Summarizers

default_forecast_accuracy_metric_set() extended_forecast_accuracy_metric_set()

Forecast Accuracy Metrics Sets

summarize_accuracy_metrics()

Summarize Accuracy Metrics

New Accuracy Metrics

maape()

Mean Arctangent Absolute Percentage Error

Parameters (Dials)

The dials parameter functions that support hyperparameter tuning with tune.

General Time Series

seasonal_period()

Tuning Parameters for Time Series (ts-class) Models

ARIMA

non_seasonal_ar() non_seasonal_differences() non_seasonal_ma() seasonal_ar() seasonal_differences() seasonal_ma()

Tuning Parameters for ARIMA Models

Exponential Smoothing

error() trend() trend_smooth() season() damping() damping_smooth() smooth_level() smooth_trend() smooth_seasonal()

Tuning Parameters for Exponential Smoothing Models

Prophet

growth() changepoint_num() changepoint_range() seasonality_yearly() seasonality_weekly() seasonality_daily() prior_scale_changepoints() prior_scale_seasonality() prior_scale_holidays()

Tuning Parameters for Prophet Models

NNETAR

num_networks()

Tuning Parameters for NNETAR Models

ADAM

use_constant() regressors_treatment() outliers_treatment() probability_model() distribution() information_criteria() select_order()

Tuning Parameters for ADAM Models

Temporal Hierachical Models

combination_method() use_model()

Tuning Parameters for TEMPORAL HIERARCHICAL Models

Developer Tools

Tools for extending modeltime.

new_modeltime_bridge()

Constructor for creating modeltime models

create_xreg_recipe()

Developer Tools for preparing XREGS (Regressors)

juice_xreg_recipe() bake_xreg_recipe()

Developer Tools for processing XREGS (Regressors)

parse_index_from_data() parse_period_from_index()

Developer Tools for parsing date and date-time information

get_model_description()

Get model descriptions for parsnip, workflows & modeltime objects

get_arima_description()

Get model descriptions for Arima objects

Data

m750

The 750th Monthly Time Series used in the M4 Competition

m750_models

Three (3) Models trained on the M750 Data (Training Set)

m750_splits

The results of train/test splitting the M750 Data

m750_training_resamples

The Time Series Cross Validation Resamples the M750 Data (Training Set)