modeltime1.3.1 package

The Tidymodels Extension for Time Series Modeling

adam_fit_impl

Low-Level ADAM function for translating modeltime to forecast

adam_params

Tuning Parameters for ADAM Models

Adam_predict_impl

Bridge prediction function for ADAM models

adam_reg

General Interface for ADAM Regression Models

add_modeltime_model

Add a Model into a Modeltime Table

arima_boost

General Interface for "Boosted" ARIMA Regression Models

Arima_fit_impl

Low-Level ARIMA function for translating modeltime to forecast

arima_params

Tuning Parameters for ARIMA Models

Arima_predict_impl

Bridge prediction function for ARIMA models

arima_reg

General Interface for ARIMA Regression Models

arima_xgboost_fit_impl

Bridge ARIMA-XGBoost Modeling function

arima_xgboost_predict_impl

Bridge prediction Function for ARIMA-XGBoost Models

auto_adam_fit_impl

Low-Level ADAM function for translating modeltime to forecast

Auto_adam_predict_impl

Bridge prediction function for AUTO ADAM models

auto_arima_fit_impl

Low-Level ARIMA function for translating modeltime to forecast

auto_arima_xgboost_fit_impl

Bridge ARIMA-XGBoost Modeling function

combine_modeltime_tables

Combine multiple Modeltime Tables into a single Modeltime Table

control_modeltime

Control aspects of the training process

create_model_grid

Helper to make parsnip model specs from a dials parameter grid

create_xreg_recipe

Developer Tools for preparing XREGS (Regressors)

croston_fit_impl

Low-Level Exponential Smoothing function for translating modeltime to ...

croston_predict_impl

Bridge prediction function for CROSTON models

dot_prepare_transform

Prepare Recursive Transformations

drop_modeltime_model

Drop a Model from a Modeltime Table

ets_fit_impl

Low-Level Exponential Smoothing function for translating modeltime to ...

ets_predict_impl

Bridge prediction function for Exponential Smoothing models

exp_smoothing_params

Tuning Parameters for Exponential Smoothing Models

exp_smoothing

General Interface for Exponential Smoothing State Space Models

get_arima_description

Get model descriptions for Arima objects

get_model_description

Get model descriptions for parsnip, workflows & modeltime objects

get_tbats_description

Get model descriptions for TBATS objects

is_calibrated

Test if a Modeltime Table has been calibrated

is_modeltime_model

Test if object contains a fitted modeltime model

is_modeltime_table

Test if object is a Modeltime Table

is_residuals

Test if a table contains residuals.

load_namespace

These are not intended for use by the general public.

log_extractors

Log Extractor Functions for Modeltime Nested Tables

maape_vec

Mean Arctangent Absolute Percentage Error

maape

Mean Arctangent Absolute Percentage Error

make_ts_splits

Generate a Time Series Train/Test Split Indicies

mdl_time_forecast

Modeltime Forecast Helpers

mdl_time_refit

Modeltime Refit Helpers

metric_sets

Forecast Accuracy Metrics Sets

modeltime_accuracy

Calculate Accuracy Metrics

modeltime_calibrate

Preparation for forecasting

modeltime_fit_workflowset

Fit a workflowset object to one or multiple time series

modeltime_forecast

Forecast future data

modeltime_nested_fit

Fit Tidymodels Workflows to Nested Time Series

modeltime_nested_forecast

Modeltime Nested Forecast

modeltime_nested_refit

Refits a Nested Modeltime Table

modeltime_nested_select_best

Select the Best Models from Nested Modeltime Table

modeltime_refit

Refit one or more trained models to new data

modeltime_residuals_test

Apply Statistical Tests to Residuals

modeltime_residuals

Extract Residuals Information

modeltime_table

Scale forecast analysis with a Modeltime Table

naive_fit_impl

Low-Level NAIVE Forecast

naive_predict_impl

Bridge prediction function for NAIVE Models

naive_reg

General Interface for NAIVE Forecast Models

new_modeltime_bridge

Constructor for creating modeltime models

nnetar_fit_impl

Low-Level NNETAR function for translating modeltime to forecast

nnetar_params

Tuning Parameters for NNETAR Models

nnetar_predict_impl

Bridge prediction function for ARIMA models

nnetar_reg

General Interface for NNETAR Regression Models

panel_tail

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

parallel_start

Start parallel clusters using parallel package

parse_index

Developer Tools for parsing date and date-time information

pipe

Pipe operator

plot_modeltime_forecast

Interactive Forecast Visualization

plot_modeltime_residuals

Interactive Residuals Visualization

pluck_modeltime_model

Extract model by model id in a Modeltime Table

prep_nested

Prepared Nested Modeltime Data

prophet_boost

General Interface for Boosted PROPHET Time Series Models

prophet_fit_impl

Low-Level PROPHET function for translating modeltime to PROPHET

prophet_params

Tuning Parameters for Prophet Models

prophet_predict_impl

Bridge prediction function for PROPHET models

prophet_reg

General Interface for PROPHET Time Series Models

prophet_xgboost_fit_impl

Low-Level PROPHET function for translating modeltime to Boosted PROPHE...

prophet_xgboost_predict_impl

Bridge prediction function for Boosted PROPHET models

pull_modeltime_residuals

Extracts modeltime residuals data from a Modeltime Model

pull_parsnip_preprocessor

Pulls the Formula from a Fitted Parsnip Model Object

recipe_helpers

Developer Tools for processing XREGS (Regressors)

recursive

Create a Recursive Time Series Model from a Parsnip or Workflow Regres...

seasonal_reg

General Interface for Multiple Seasonality Regression Models (TBATS, S...

smooth_fit_impl

Low-Level Exponential Smoothing function for translating modeltime to ...

smooth_predict_impl

Bridge prediction function for Exponential Smoothing models

snaive_fit_impl

Low-Level SNAIVE Forecast

snaive_predict_impl

Bridge prediction function for SNAIVE Models

stlm_arima_fit_impl

Low-Level stlm function for translating modeltime to forecast

stlm_arima_predict_impl

Bridge prediction function for ARIMA models

stlm_ets_fit_impl

Low-Level stlm function for translating modeltime to forecast

stlm_ets_predict_impl

Bridge prediction function for ARIMA models

summarize_accuracy_metrics

Summarize Accuracy Metrics

table_modeltime_accuracy

Interactive Accuracy Tables

tbats_fit_impl

Low-Level tbats function for translating modeltime to forecast

tbats_predict_impl

Bridge prediction function for ARIMA models

temporal_hier_fit_impl

Low-Level Temporaral Hierarchical function for translating modeltime t...

temporal_hier_predict_impl

Bridge prediction function for TEMPORAL HIERARCHICAL models

temporal_hierarchy_params

Tuning Parameters for TEMPORAL HIERARCHICAL Models

temporal_hierarchy

General Interface for Temporal Hierarchical Forecasting (THIEF) Models

theta_fit_impl

Low-Level Exponential Smoothing function for translating modeltime to ...

theta_predict_impl

Bridge prediction function for THETA models

tidyeval

Tidy eval helpers

time_series_params

Tuning Parameters for Time Series (ts-class) Models

type_sum.mdl_time_tbl

Succinct summary of Modeltime Tables

update_model_description

Update the model description by model id in a Modeltime Table

update_modeltime_model

Update the model by model id in a Modeltime Table

window_function_fit_impl

Low-Level Window Forecast

window_function_predict_impl

Bridge prediction function for window Models

window_reg

General Interface for Window Forecast Models

xgboost_impl

Wrapper for parsnip::xgb_train

xgboost_predict

Wrapper for xgboost::predict

The time series forecasting framework for use with the 'tidymodels' ecosystem. Models include ARIMA, Exponential Smoothing, and additional time series models from the 'forecast' and 'prophet' packages. Refer to "Forecasting Principles & Practice, Second edition" (<https://otexts.com/fpp2/>). Refer to "Prophet: forecasting at scale" (<https://research.facebook.com/blog/2017/02/prophet-forecasting-at-scale/>.).

  • Maintainer: Matt Dancho
  • License: MIT + file LICENSE
  • Last published: 2024-10-22