Microsoft Finance Time Series Forecasting Framework
c(person(given = "Mike",
family = "Tokic",
role = c("aut", "cre"),
email = "mftokic@gmail.com",
comment = c(ORCID = "0000-0002-7630-7055")),
person(given = "Aadharsh",
family = "Kannan",
role = c("aut"),
email = "akannan@microsoft.com",
comment = c(ORCID = "0000-0002-6475-8211")))
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 want 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!
cli, Cubist, dials, doParallel, dplyr, earth, foreach,
generics, glmnet, gtools, hts, kernlab, lubridate, magrittr,
methods, modeltime.gluonts, modeltime.resample, parallel,
parsnip, plyr, purrr, recipes, rsample, rules, stringr, tibble,
tidyr, tidyselect, timetk, tune, workflows
knitr, reactable, rmarkdown, testthat (>= 3.0.0)

knitr, reactable, rmarkdown, sparklyr, testthat (>= 3.0.0)

Financial Data from U.S. Securities and Exchange Commission
Download and display company financial data from the U.S. Securities
and Exchange Commission's EDGAR database. It contains a suite of functions with
web scraping and XBRL parsing capabilities that allows users to extract data from EDGAR
in an automated and scalable manner. See <https://www.sec.gov/edgar/searchedgar/companysearch.html>
for more information.

Access to Average Purchase Price of Vehicles in Brazil
person("Italo", "Cegatta", email = "italocegatta@gmail.com", role = c("aut", "cre"))
The Brazilian vehicle purchase pricing table is provided by
the Institute of Economic Research Foundation (Fipe) and used in purchase
negotiations according to region, vehicle’s conservation, color, accessories
or any other factor that might influence the demand and supply for a specific
vehicle. For more on the data themselves and web access, please see
<https://www.fipe.org.br/en-us/home/>.
httr, jsonlite, dplyr, tibble, lubridate, tidyr, stringr,
readr, magrittr, purrr, furrr, forcats, future