Modeltime Workflow

The main workflow functions for scalable time series modeling.

Core Functions

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_residuals()

Extract Residuals Information

modeltime_refit()

Refit one or more trained models to new data

Plotting & Tables

plot_modeltime_forecast()

Interactive Forecast Visualization

plot_modeltime_residuals()

Interactive Residuals Visualization

table_modeltime_accuracy()

Interactive Accuracy Tables

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_model_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

default_forecast_accuracy_metric_set()

Forecast Accuracy Metrics Sets

Algorithms

The parsnip-adjacent algorithms that implement time series models.

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

Parameters

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() season() damping()

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

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)

arima_workflow_tuned

Example ARIMA Tuning Results