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https://github.com/microsoft/finnts

Microsoft Finance Time Series Forecasting Framework (FinnTS) is a forecasting package that utilizes cutting-edge time series forecasting and parallelization on the cloud to produce accurate forecasts for financial data.

https://github.com/microsoft/finnts

Science Score: 26.0%

This score indicates how likely this project is to be science-related based on various indicators:

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    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
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    .zenodo.json file
    Found .zenodo.json file
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    DOI references
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    Academic publication links
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    Committers with academic emails
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    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (15.8%) to scientific vocabulary

Keywords

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

Keywords from Contributors

large-language-model logistics
Last synced: 5 months ago · JSON representation

Repository

Microsoft Finance Time Series Forecasting Framework (FinnTS) is a forecasting package that utilizes cutting-edge time series forecasting and parallelization on the cloud to produce accurate forecasts for financial data.

Basic Info
Statistics
  • Stars: 207
  • Watchers: 13
  • Forks: 41
  • Open Issues: 9
  • Releases: 11
Topics
business data-science feature-selection finance finnts forecasting machine-learning microsoft r r-package rstats time-series
Created over 4 years ago · Last pushed 6 months ago
Metadata Files
Readme Changelog License Code of conduct Security Support

README.md

Microsoft Finance Time Series Forecasting Framework

CRAN_Status_Badge

The Microsoft Finance Time Series Forecasting Framework, aka finnts or Finn, is an automated forecasting framework for producing financial forecasts. While it was built for corporate finance activities, it can easily expand to any time series forecasting problem!

  • Built in AI agent that can act as your own virtual data scientist, always optimizing for the most accurate forecast.
  • Automated feature engineering, feature selection, back testing, and model selection.
  • Access to 25+ models. Both univariate and multivariate models.
  • Azure integration to run thousands of time series in parallel within the cloud.
  • Supports daily, weekly, monthly, quarterly, and yearly forecasts.
  • Handles external regressors, either purely historical or historical+future values.

Installation

CRAN version

r install.packages("finnts")

Development version

To get a bug fix or to use a feature from the development version, you can install the development version of finnts from GitHub.

``` r

install.packages("devtools")

devtools::install_github("microsoft/finnts") ```

Usage

``` r library(finnts)

prepare historical data

histdata <- timetk::m4monthly %>% dplyr::rename(Date = date) %>% dplyr::mutate(id = as.character(id))

connect LLM

driverllm <- ellmer::chatazure_openai(model = "gpt-4o-mini")

set up new forecast project and agent run

project <- setprojectinfo(projectname = "DemoProject", combovariables = c("id"), targetvariable = "value", date_type = "month")

agent <- setagentinfo(projectinfo = project, driverllm = driverllm, inputdata = histdata, forecasthorizon = 6)

iterate forecast via agent

iterateforecast(agentinfo = agent, maxiter = 3, weightedmape_goal = 0.03)

load final forecast output

forecastoutput <- getagentforecast(agentinfo = agent) ```

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode\@microsoft.com with any additional questions or comments.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.

Owner

  • Name: Microsoft
  • Login: microsoft
  • Kind: organization
  • Email: opensource@microsoft.com
  • Location: Redmond, WA

Open source projects and samples from Microsoft

GitHub Events

Total
  • Create event: 6
  • Issues event: 13
  • Release event: 1
  • Watch event: 19
  • Delete event: 6
  • Member event: 1
  • Push event: 154
  • Pull request review comment event: 2
  • Pull request review event: 12
  • Pull request event: 14
  • Fork event: 6
Last Year
  • Create event: 6
  • Issues event: 13
  • Release event: 1
  • Watch event: 19
  • Delete event: 6
  • Member event: 1
  • Push event: 154
  • Pull request review comment event: 2
  • Pull request review event: 12
  • Pull request event: 14
  • Fork event: 6

Committers

Last synced: 9 months ago

All Time
  • Total Commits: 657
  • Total Committers: 8
  • Avg Commits per committer: 82.125
  • Development Distribution Score (DDS): 0.081
Past Year
  • Commits: 53
  • Committers: 1
  • Avg Commits per committer: 53.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Mike Tokic m****c@m****m 604
Aadharsh Kannan a****n@m****m 21
Taichi Kato t****n@g****m 11
Leonardo Bretado Aguirre l****r@m****m 9
Microsoft Open Source m****e 5
Daniel Sousa-Lennox Mejia d****u@m****m 4
Lionel Henry l****y@g****m 2
microsoft-github-operations[bot] 5****] 1
Committer Domains (Top 20 + Academic)
microsoft.com: 4

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 73
  • Total pull requests: 106
  • Average time to close issues: about 1 year
  • Average time to close pull requests: 7 days
  • Total issue authors: 10
  • Total pull request authors: 8
  • Average comments per issue: 0.27
  • Average comments per pull request: 0.28
  • Merged pull requests: 91
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 15
  • Average time to close issues: N/A
  • Average time to close pull requests: 5 days
  • Issue authors: 0
  • Pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.0
  • Merged pull requests: 12
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • mitokic (61)
  • AKannanMSFT (4)
  • MislavSag (1)
  • vidarsumo (1)
  • AndrewKostandy (1)
  • talegari (1)
  • HenrikBengtsson (1)
  • amitk1993 (1)
  • hinsonl (1)
  • mdancho84 (1)
Pull Request Authors
  • mitokic (91)
  • DSLmsft (4)
  • leon-dan (3)
  • laresbernardo (2)
  • AKannanMSFT (2)
  • wesstone12 (2)
  • taixhi (1)
  • lionel- (1)
Top Labels
Issue Labels
enhancement (35) bug (14) launch criteria (13) documentation (6) R (2) question (2) good first issue (1)
Pull Request Labels
bug (33) enhancement (12) documentation (6) launch criteria (3)

Packages

  • Total packages: 2
  • Total downloads:
    • cran 724 last-month
  • Total docker downloads: 21,613
  • Total dependent packages: 0
    (may contain duplicates)
  • Total dependent repositories: 0
    (may contain duplicates)
  • Total versions: 21
  • Total maintainers: 1
proxy.golang.org: github.com/microsoft/finnts
  • Versions: 10
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent packages count: 6.4%
Average: 6.6%
Dependent repos count: 6.8%
Last synced: 6 months ago
cran.r-project.org: finnts

Microsoft Finance Time Series Forecasting Framework

  • Versions: 11
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 724 Last month
  • Docker Downloads: 21,613
Rankings
Forks count: 4.3%
Stargazers count: 5.4%
Average: 18.7%
Dependent packages count: 29.8%
Dependent repos count: 35.5%
Maintainers (1)
mftokic@gmail.com
Last synced: 6 months ago

Dependencies

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.github/workflows/pkgdown.yaml actions
  • actions/checkout v2 composite
  • r-lib/actions/setup-pandoc v1 composite
  • r-lib/actions/setup-r v1 composite
DESCRIPTION cran
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