Package: finnts 0.5.0.9001

Mike Tokic

finnts: Microsoft Finance Time Series Forecasting Framework

Automated time series forecasting developed by Microsoft Finance. The Microsoft Finance Time Series Forecasting Framework, aka Finn, can be used to forecast any component of the income statement, balance sheet, or any other area of interest by finance. Any numerical quantity over time, Finn can be used to forecast it. While it can be applied outside of the finance domain, Finn was built to meet the needs of financial analysts to better forecast their businesses within a company, and has a lot of built in features that are specific to the needs of financial forecasters. Happy forecasting!

Authors:Mike Tokic [aut, cre], Aadharsh Kannan [aut]

finnts_0.5.0.9001.tar.gz
finnts_0.5.0.9001.zip(r-4.5)finnts_0.5.0.9001.zip(r-4.4)finnts_0.5.0.9001.zip(r-4.3)
finnts_0.5.0.9001.tgz(r-4.4-any)finnts_0.5.0.9001.tgz(r-4.3-any)
finnts_0.5.0.9001.tar.gz(r-4.5-noble)finnts_0.5.0.9001.tar.gz(r-4.4-noble)
finnts_0.5.0.9001.tgz(r-4.4-emscripten)finnts_0.5.0.9001.tgz(r-4.3-emscripten)
finnts.pdf |finnts.html✨
finnts/json (API)
NEWS

# Install 'finnts' in R:

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

Bug tracker:https://github.com/microsoft/finnts/issues

On CRAN:

business data-science feature-selection finance finnts forecasting machine-learning microsoft time-series

9.50 score 187 stars 39 scripts 493 downloads 31 exports 206 dependencies

Last updated 25 days agofrom:370d625f83. Checks:OK: 7. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Oct 29 2024
R-4.5-win	OK	Oct 29 2024
R-4.5-linux	OK	Oct 29 2024
R-4.4-win	OK	Oct 29 2024
R-4.4-mac	OK	Oct 29 2024
R-4.3-win	OK	Oct 29 2024
R-4.3-mac	OK	Oct 29 2024

Exports:cubist_multistep cubist_multistep_fit_impl cubist_multistep_predict_impl ensemble_models final_models forecast_time_series get_forecast_data get_prepped_data get_prepped_models get_run_info get_trained_models glmnet_multistep glmnet_multistep_fit_impl glmnet_multistep_predict_impl list_models mars_multistep mars_multistep_fit_impl mars_multistep_predict_impl prep_data prep_models set_run_info svm_poly_multistep svm_poly_multistep_fit_impl svm_poly_multistep_predict_impl svm_rbf_multistep svm_rbf_multistep_fit_impl svm_rbf_multistep_predict_impl train_models xgboost_multistep xgboost_multistep_fit_impl xgboost_multistep_predict_impl

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 Cubist curl data.table desc diagram dials DiceDesign digest distributional doFuture doParallel dplyr dygraphs earth ellipsis evaluate extraDistr fabletools fansi farver fastmap feasts fontawesome forcats foreach forecast Formula fracdiff fs furrr future future.apply generics ggdist ggplot2 glmnet globals glue gower GPfit gridExtra gt gtable gtools hardhat highr hms htmltools htmlwidgets hts httr infer inline ipred isoband iterators janitor jquerylib jsonlite juicyjuice kernlab KernSmooth knitr labeling later lattice lava lazyeval lhs lifecycle listenv lmtest loo lubridate magrittr markdown MASS Matrix matrixStats memoise mgcv mime modeldata modelenv modeltime munsell nlme nnet numDeriv openssl padr parallelly parsnip patchwork pillar pkgbuild pkgconfig plotly plotmo plotrix plyr 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 reshape2 rlang rmarkdown rpart rsample rstan rstantools rstudioapi rules sass scales sfd shape slider snakecase SparseM SQUAREM StanHeaders stringi stringr survival sys tensorA tibble tidymodels tidyr tidyselect timechange timeDate timetk tinytex tseries tsfeatures tsibble TTR tune tzdb urca utf8 V8 vctrs viridisLite vroom warp withr workflows workflowsets xfun xgboost xml2 xts yaml yardstick zoo