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!

Microsoft Finance Time Series Forecasting Framework

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

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.

# install.packages("devtools")
devtools::install_github("microsoft/finnts")

Optional variable importance support

Feature selection uses the optional Boruta, corrr, ranger, and vip packages. Model-summary variable importance uses vip 0.5.0 or newer. Because vip is distributed from its maintainer's r-universe repository, install the optional stack separately when you need those capabilities:

install.packages(
  c("Boruta", "corrr", "ranger", "vip"),
  repos = c(
    "https://bgreenwell.r-universe.dev",
    "https://cloud.r-project.org"
  )
)

vip 0.5.0 requires R 4.1 or newer. FinnTS still installs and runs its default forecasting workflows without these optional packages: requesting feature selection reports which dependency to install, while model summaries omit only variable-importance rows when vip is unavailable.

Usage

library(finnts)

# prepare historical data
hist_data <- timetk::m4_monthly %>%
  dplyr::rename(Date = date) %>%
  dplyr::mutate(id = as.character(id))
  
# connect LLM
llm <- ellmer::chat_azure_openai(model = "gpt-4o-mini")

# set up new forecast project and agent run
project <- set_project_info(project_name = "Demo_Project", 
                            combo_variables = c("id"),
                            target_variable = "value",
                            date_type = "month")
                            
agent <- set_agent_info(project_info = project,
                        llm = llm,
                        input_data = hist_data,
                        forecast_horizon = 6)

# iterate forecast via agent
iterate_forecast(agent_info = agent,
                 max_iter = 3,
                 weighted_mape_goal = 0.03)

# load final forecast output
forecast_output <- get_agent_forecast(agent_info = agent)

# ask agent to explain the forecast results
eda_answer <- ask_agent(agent_info = agent,
                        question = "Summarize the exploratory data analysis, what stands out?")
                        
accuracy_answer <- ask_agent(agent_info = agent,
                             question = "What's the best model accuracy?")
                             
model_answer <- ask_agent(agent_info = agent,
                          question = "Explain why the best model was selected. How does that model work?")

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 [email protected] 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.