Package: OOS 1.0.0

OOS: Out-of-Sample Time Series Forecasting

A comprehensive and cohesive API for the out-of-sample forecasting workflow: data preparation, forecasting - including both traditional econometric time series models and modern machine learning techniques - forecast combination, model and error analysis, and forecast visualization.

Authors:Tyler J. Pike [aut, cre]

OOS_1.0.0.tar.gz
OOS_1.0.0.zip(r-4.5)OOS_1.0.0.zip(r-4.4)OOS_1.0.0.zip(r-4.3)
OOS_1.0.0.tgz(r-4.4-any)OOS_1.0.0.tgz(r-4.3-any)
OOS_1.0.0.tar.gz(r-4.5-noble)OOS_1.0.0.tar.gz(r-4.4-noble)
OOS_1.0.0.tgz(r-4.4-emscripten)OOS_1.0.0.tgz(r-4.3-emscripten)
OOS.pdf |OOS.html✨
OOS/json (API)

# Install 'OOS' in R:

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

Bug tracker:https://github.com/tylerjpike/oos/issues

On CRAN:

econometrics forecast-combination forecasting machine-learning

4.65 score 9 stars 5 scripts 120 downloads 22 exports 104 dependencies

Last updated 4 years agofrom:3de023f38e. Checks:OK: 7. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Oct 30 2024
R-4.5-win	OK	Oct 30 2024
R-4.5-linux	OK	Oct 30 2024
R-4.4-win	OK	Oct 30 2024
R-4.4-mac	OK	Oct 30 2024
R-4.3-win	OK	Oct 30 2024
R-4.3-mac	OK	Oct 30 2024

Exports:%>%chart_forecast chart_forecast_error data_impute data_outliers data_reduction data_subset forecast_accuracy forecast_combine forecast_comparison forecast_multivariate forecast_univariate instantiate.data_impute.control_panel instantiate.forecast_combinations.control_panel instantiate.forecast_multivariate.ml.control_panel instantiate.forecast_multivariate.var.control_panel instantiate.forecast_univariate.control_panel loss_function n.lag NBest standardize winsorize

Dependencies:caret class cli clock codetools colorspace commonmark cpp11 curl data.table diagram digest dplyr e1071 fansi farver foreach forecast fracdiff furrr future future.apply generics ggplot2 ggtext glmnet globals glue gower gridtext gtable hardhat imputeTS ipred isoband iterators jpeg jsonlite KernSmooth labeling lattice lava lifecycle listenv lmtest lubridate magrittr markdown MASS Matrix mgcv ModelMetrics munsell nlme nnet numDeriv parallelly pillar pkgconfig plyr png pROC prodlim progressr proxy purrr quadprog quantmod R6 RColorBrewer Rcpp RcppArmadillo RcppEigen recipes reshape2 rlang rpart sandwich scales shape SQUAREM stinepack stringi stringr strucchange survival tibble tidyr tidyselect timechange timeDate tseries TTR tzdb urca utf8 vars vctrs viridisLite withr xfun xml2 xts zoo

Basic Introduction to OOS

Rendered frombasic_introduction.Rmdusingknitr::rmarkdownon Oct 30 2024.

Last update: 2021-03-07
Started: 2021-02-28

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Chart forecasts	chart_forecast
Chart forecast errors	chart_forecast_error
Impute missing values	data_impute
Clean outliers	data_outliers
Dimension reduction via principal components	data_reduction
Create information set	data_subset
Calculate forecast accuracy	forecast_accuracy
Forecast with forecast combinations	forecast_combine
Compare forecast accuracy	forecast_comparison
Set forecasted date	forecast_date
Forecast with multivariate models	forecast_multivariate
Forecast with univariate models	forecast_univariate
Create interface to control 'data_impute' model estimation	instantiate.data_impute.control_panel
Create interface to control 'forecast_combine' model estimation	instantiate.forecast_combinations.control_panel
Create interface to control 'forecast_multivariate' ML estimation	instantiate.forecast_multivariate.ml.control_panel
Create interface to control 'forecast_multivariate' VAR estimation	instantiate.forecast_multivariate.var.control_panel
Create interface to control 'forecast_univariate' model estimation	instantiate.forecast_univariate.control_panel
Calculate error via loss functions	loss_function
Create n lags	n.lag
Select N-best forecasts	NBest
Standardize variables (mean 0, variance 1)	standardize
Winsorize or trim variables	winsorize