Package: caretForecast 0.1.1

Resul Akay

caretForecast: Conformal Time Series Forecasting Using State of Art Machine Learning Algorithms

Conformal time series forecasting using the caret infrastructure. It provides access to state-of-the-art machine learning models for forecasting applications. The hyperparameter of each model is selected based on time series cross-validation, and forecasting is done recursively.

Authors:Resul Akay [aut, cre]

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caretForecast.pdf |caretForecast.html✨
caretForecast/json (API)
NEWS

# Install 'caretForecast' in R:

install.packages('caretForecast', repos = c('https://akai01.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/akai01/caretforecast/issues

Datasets:

retail - Grouped sales data from an Australian Retailer
retail_wide - Sales data from an Australian Retailer in time series format

On CRAN:

caret conformal-prediction data-science econometrics forecast forecasting forecasting-models machine-learning macroeconometrics microeconometrics time-series time-series-forcasting time-series-prediction

5.61 score 24 stars 4 packages 28 scripts 393 downloads 11 exports 88 dependencies

Last updated 2 years agofrom:d3b1159463. Checks:OK: 7. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Nov 11 2024
R-4.5-win	OK	Nov 11 2024
R-4.5-linux	OK	Nov 11 2024
R-4.4-win	OK	Nov 11 2024
R-4.4-mac	OK	Nov 11 2024
R-4.3-win	OK	Nov 11 2024
R-4.3-mac	OK	Nov 11 2024

Exports:%<>%%>%accuracy ARml autolayer autoplot conformalRegressor forecast get_var_imp split_ts suggested_methods

Dependencies:caret class cli clock codetools colorspace cpp11 curl data.table diagram digest dplyr e1071 fansi farver foreach forecast fracdiff future future.apply generics ggplot2 globals glue gower gtable hardhat ipred isoband iterators jsonlite KernSmooth labeling lattice lava lifecycle listenv lmtest lubridate magrittr MASS Matrix mgcv ModelMetrics munsell nlme nnet numDeriv parallelly pillar pkgconfig plyr pROC prodlim progressr proxy purrr quadprog quantmod R6 RColorBrewer Rcpp RcppArmadillo recipes reshape2 rlang rpart scales shape SQUAREM stringi stringr survival tibble tidyr tidyselect timechange timeDate tseries TTR tzdb urca utf8 vctrs viridisLite withr xts zoo

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Autoregressive forecasting using various Machine Learning models.	ARml
Fit a conformal regressor.	conformalRegressor
Forecasting using ARml model	forecast.ARml
Variable importance for forecasting model.	get_var_imp
Predict a conformalRegressor	predict.conformalRegressor
Grouped sales data from an Australian Retailer	retail
Sales data from an Australian Retailer in time series format	retail_wide
Split a time series into training and testing sets	split_ts
Suggested methods for ARml	suggested_methods