Package: modeltime 1.3.0

Matt Dancho

modeltime:The Tidymodels Extension for Time Series Modeling

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/>.).

Authors:Matt Dancho [aut, cre], Business Science [cph]

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modeltime.pdf |modeltime.html✨
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# Installmodeltime in R:

install.packages('modeltime',repos = c('https://business-science.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/business-science/modeltime/issues

Datasets:

m750 - The 750th Monthly Time Series used in the M4 Competition
m750_models - Three
m750_splits - The results of train/test splitting the M750 Data
m750_training_resamples - The Time Series Cross Validation Resamples the M750 Data

On CRAN:

arima data-science deep-learning ets forecasting machine-learning machine-learning-algorithms modeltime prophet tbats tidymodeling tidymodels time time-series time-series-analysis timeseries timeseries-forecasting

167 exports 506 stars 7.51 score 187 dependencies 5 dependents 2.6k downloads

Last updated 6 months agofrom:999e99b8c1

Exports::=.data .prepare_panel_transform .prepare_transform %>%adam_fit_impl Adam_predict_impl adam_reg add_modeltime_model arima_boost Arima_fit_impl Arima_predict_impl arima_reg arima_xgboost_fit_impl arima_xgboost_predict_impl as_label as_modeltime_table as_name auto_adam_fit_impl Auto_adam_predict_impl auto_arima_fit_impl auto_arima_xgboost_fit_impl bake_xreg_recipe changepoint_num changepoint_range combination_method combine_modeltime_tables control_fit_workflowset control_nested_fit control_nested_forecast control_nested_refit control_refit create_model_grid create_xreg_recipe croston_fit_impl croston_predict_impl damping damping_smooth default_forecast_accuracy_metric_set distribution drop_modeltime_model enquo enquos error ets_fit_impl ets_predict_impl exp_smoothing expr extend_timeseries extended_forecast_accuracy_metric_set extract_nested_best_model_report extract_nested_error_report extract_nested_future_forecast extract_nested_modeltime_table extract_nested_test_accuracy extract_nested_test_forecast extract_nested_test_split extract_nested_train_split get_arima_description get_model_description get_tbats_description growth information_criteria is_calibrated is_modeltime_model is_modeltime_table is_residuals juice_xreg_recipe load_namespace maape maape_vec make_ts_splits mdl_time_forecast mdl_time_refit modeltime_accuracy modeltime_calibrate modeltime_fit_workflowset modeltime_forecast modeltime_nested_fit modeltime_nested_forecast modeltime_nested_refit modeltime_nested_select_best modeltime_refit modeltime_residuals modeltime_residuals_test modeltime_table naive_fit_impl naive_predict_impl naive_reg nest_timeseries new_modeltime_bridge nnetar_fit_impl nnetar_predict_impl nnetar_reg non_seasonal_ar non_seasonal_differences non_seasonal_ma num_networks outliers_treatment panel_tail parallel_start parallel_stop parse_index_from_data parse_period_from_index plot_modeltime_forecast plot_modeltime_residuals pluck_modeltime_model prior_scale_changepoints prior_scale_holidays prior_scale_seasonality probability_model prophet_boost prophet_fit_impl prophet_predict_impl prophet_reg prophet_xgboost_fit_impl prophet_xgboost_predict_impl pull_modeltime_model pull_modeltime_residuals pull_parsnip_preprocessor recursive regressors_treatment season seasonal_ar seasonal_differences seasonal_ma seasonal_period seasonal_reg seasonality_daily seasonality_weekly seasonality_yearly select_order smooth_fit_impl smooth_level smooth_predict_impl smooth_seasonal smooth_trend snaive_fit_impl snaive_predict_impl split_nested_timeseries stlm_arima_fit_impl stlm_arima_predict_impl stlm_ets_fit_impl stlm_ets_predict_impl summarize_accuracy_metrics sym syms table_modeltime_accuracy tbats_fit_impl tbats_predict_impl temporal_hier_fit_impl temporal_hier_predict_impl temporal_hierarchy theta_fit_impl theta_predict_impl trend trend_smooth update_model_description update_modeltime_description update_modeltime_model use_constant use_model window_function_fit_impl window_function_predict_impl window_reg xgboost_impl xgboost_predict

Dependencies:abind anytime askpass backports base64enc BH bigD bit bit64 bitops broom bslib cachem callr checkmate class cli clipr clock codetools colorspace commonmark conflicted cpp11 crayon crosstalk curl data.table desc diagram dials DiceDesign digest distributional doFuture doParallel dplyr dygraphs ellipsis evaluate extraDistr fansi farver fastmap fontawesome forcats foreach forecast fracdiff fs furrr future future.apply generics ggplot2 globals glue gower GPfit gridExtra gt gtable hardhat highr hms htmltools htmlwidgets httr infer inline ipred isoband iterators janitor jquerylib jsonlite juicyjuice KernSmooth knitr labeling later lattice lava lazyeval lhs lifecycle listenv lmtest loo lubridate magrittr markdown MASS Matrix matrixStats memoise mgcv mime modeldata modelenv munsell nlme nnet numDeriv openssl padr parallelly parsnip patchwork pillar pkgbuild pkgconfig plotly posterior prettyunits processx prodlim progress progressr promises prophet ps purrr quadprog quantmod QuickJSR R6 rappdirs RColorBrewer Rcpp RcppArmadillo RcppEigen RcppParallel RcppRoll reactable reactR readr recipes rlang rmarkdown rpart rsample rstan rstantools rstudioapi sass scales shape slider snakecase SQUAREM StanHeaders stringi stringr survival sys tensorA tibble tidymodels tidyr tidyselect timechange timeDate timetk tinytex tseries tsfeatures TTR tune tzdb urca utf8 V8 vctrs viridisLite vroom warp withr workflows workflowsets xfun xgboost xml2 xts yaml yardstick zoo

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Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Tuning Parameters for ADAM Models	adam_params distribution information_criteria outliers_treatment probability_model regressors_treatment select_order use_constant
General Interface for ADAM Regression Models	adam_reg
Add a Model into a Modeltime Table	add_modeltime_model
General Interface for "Boosted" ARIMA Regression Models	arima_boost
Tuning Parameters for ARIMA Models	arima_params non_seasonal_ar non_seasonal_differences non_seasonal_ma seasonal_ar seasonal_differences seasonal_ma
General Interface for ARIMA Regression Models	arima_reg
Combine multiple Modeltime Tables into a single Modeltime Table	combine_modeltime_tables
Control aspects of the training process	control_fit_workflowset control_modeltime control_nested_fit control_nested_forecast control_nested_refit control_refit
Helper to make 'parsnip' model specs from a 'dials' parameter grid	create_model_grid
Developer Tools for preparing XREGS (Regressors)	create_xreg_recipe
Drop a Model from a Modeltime Table	drop_modeltime_model
General Interface for Exponential Smoothing State Space Models	exp_smoothing
Tuning Parameters for Exponential Smoothing Models	damping damping_smooth error exp_smoothing_params season smooth_level smooth_seasonal smooth_trend trend trend_smooth
Get model descriptions for Arima objects	get_arima_description
Get model descriptions for parsnip, workflows & modeltime objects	get_model_description
Get model descriptions for TBATS objects	get_tbats_description
Log Extractor Functions for Modeltime Nested Tables	extract_nested_best_model_report extract_nested_error_report extract_nested_future_forecast extract_nested_modeltime_table extract_nested_test_accuracy extract_nested_test_forecast extract_nested_test_split extract_nested_train_split log_extractors
The 750th Monthly Time Series used in the M4 Competition	m750
Three (3) Models trained on the M750 Data (Training Set)	m750_models
The results of train/test splitting the M750 Data	m750_splits
The Time Series Cross Validation Resamples the M750 Data (Training Set)	m750_training_resamples
Mean Arctangent Absolute Percentage Error	maape
Mean Arctangent Absolute Percentage Error	maape_vec
Forecast Accuracy Metrics Sets	default_forecast_accuracy_metric_set extended_forecast_accuracy_metric_set metric_sets
Calculate Accuracy Metrics	modeltime_accuracy
Preparation for forecasting	modeltime_calibrate
Fit a 'workflowset' object to one or multiple time series	modeltime_fit_workflowset
Forecast future data	modeltime_forecast
Fit Tidymodels Workflows to Nested Time Series	modeltime_nested_fit
Modeltime Nested Forecast	modeltime_nested_forecast
Refits a Nested Modeltime Table	modeltime_nested_refit
Select the Best Models from Nested Modeltime Table	modeltime_nested_select_best
Refit one or more trained models to new data	modeltime_refit
Extract Residuals Information	modeltime_residuals
Apply Statistical Tests to Residuals	modeltime_residuals_test
Scale forecast analysis with a Modeltime Table	as_modeltime_table modeltime_table
General Interface for NAIVE Forecast Models	naive_reg
Constructor for creating modeltime models	new_modeltime_bridge
Tuning Parameters for NNETAR Models	nnetar_params num_networks
General Interface for NNETAR Regression Models	nnetar_reg
Filter the last N rows (Tail) for multiple time series	panel_tail
Start parallel clusters using 'parallel' package	parallel_start parallel_stop
Developer Tools for parsing date and date-time information	parse_index parse_index_from_data parse_period_from_index
Interactive Forecast Visualization	plot_modeltime_forecast
Interactive Residuals Visualization	plot_modeltime_residuals
Extract model by model id in a Modeltime Table	pluck_modeltime_model pluck_modeltime_model.mdl_time_tbl pull_modeltime_model
Prepared Nested Modeltime Data	extend_timeseries nest_timeseries prep_nested split_nested_timeseries
General Interface for Boosted PROPHET Time Series Models	prophet_boost
Tuning Parameters for Prophet Models	changepoint_num changepoint_range growth prior_scale_changepoints prior_scale_holidays prior_scale_seasonality prophet_params seasonality_daily seasonality_weekly seasonality_yearly
General Interface for PROPHET Time Series Models	prophet_reg
Extracts modeltime residuals data from a Modeltime Model	pull_modeltime_residuals
Pulls the Formula from a Fitted Parsnip Model Object	pull_parsnip_preprocessor
Developer Tools for processing XREGS (Regressors)	bake_xreg_recipe juice_xreg_recipe recipe_helpers
Create a Recursive Time Series Model from a Parsnip or Workflow Regression Model	recursive
General Interface for Multiple Seasonality Regression Models (TBATS, STLM)	seasonal_reg
Summarize Accuracy Metrics	summarize_accuracy_metrics
Interactive Accuracy Tables	table_modeltime_accuracy
General Interface for Temporal Hierarchical Forecasting (THIEF) Models	temporal_hierarchy
Tuning Parameters for TEMPORAL HIERARCHICAL Models	combination_method temporal_hierarchy_params use_model
Tuning Parameters for Time Series (ts-class) Models	seasonal_period time_series_params
Update the model description by model id in a Modeltime Table	update_modeltime_description update_model_description
Update the model by model id in a Modeltime Table	update_modeltime_model
General Interface for Window Forecast Models	window_reg