Skip to contents

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

Workflow

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
pull_modeltime_residuals()
Extracts modeltime residuals data from a Modeltime Model
pull_parsnip_preprocessor()
Pulls the Formula from a Fitted Parsnip Model Object

Accuracy Metrics (Yardstick)

Metric Sets and Summarizers

New Accuracy Metrics

maape()
Mean Arctangent Absolute Percentage Error
maape_vec()
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

Exponential Smoothing

Prophet

NNETAR

num_networks()
Tuning Parameters for NNETAR Models

ADAM

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
get_tbats_description()
Get model descriptions for TBATS 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)