Package: caretForecast 0.1.3

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]

caretForecast_0.1.3.tar.gz
caretForecast_0.1.3.zip(r-4.7-any)caretForecast_0.1.3.zip(r-4.6-any)caretForecast_0.1.3.zip(r-4.5-any)
caretForecast_0.1.3.tgz(r-4.6-any)caretForecast_0.1.3.tgz(r-4.5-any)
caretForecast_0.1.3.tar.gz(r-4.7-any)caretForecast_0.1.3.tar.gz(r-4.6-any)
caretForecast_0.1.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
caretForecast/json (API)

# Install 'caretForecast' in R:

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

Bug tracker:https://github.com/taf-society/caretforecast/issues

Pkgdown/docs site:https://taf-society.github.io

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

5.81 score 29 stars 4 packages 37 scripts 529 downloads 12 exports 80 dependencies

Last updated from:af9e337619. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel	OK	267
source / vignettes	OK	210
linux-release	OK	226
macos-release	OK	227
macos-oldrel	OK	195
windows-devel	OK	159
windows-release	OK	170
windows-oldrel	OK	165
wasm-release	OK	126

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

Dependencies:caret class cli clock codetools colorspace cpp11 data.table diagram digest dplyr e1071 farver foreach forecast fracdiff future future.apply generics ggplot2 globals glue gower gtable hardhat ipred isoband iterators KernSmooth labeling lattice lava lifecycle listenv lmtest lubridate magrittr MASS Matrix ModelMetrics nlme nnet numDeriv parallelly pillar pkgconfig plyr pROC prodlim progressr proxy purrr R6 RColorBrewer Rcpp RcppArmadillo recipes reshape2 rlang rpart S7 scales shape sparsevctrs SQUAREM stringi stringr survival tibble tidyr tidyselect timechange timeDate tzdb urca utf8 vctrs viridisLite withr 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
Fit a horizon-specific conformal regressor for time series forecasting.	conformalRegressorByHorizon
Forecasting using ARml model	forecast.ARml
Variable importance for forecasting model.	get_var_imp
Predict a conformalRegressor	predict.conformalRegressor
Predict intervals from a horizon-specific conformal regressor	predict.conformalRegressorByHorizon
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