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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.

install.packages("finnts")

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")

library(finnts)# prepare historical datahist_data<-timetk::m4_monthly %>%dplyr::rename(Date=date) %>%dplyr::mutate(id= as.character(id))# connect LLMdriver_llm<-ellmer::chat_azure_openai(model="gpt-4o-mini")# set up new forecast project and agent runproject<- set_project_info(project_name="Demo_Project",combo_variables= c("id"),target_variable="value",date_type="month")agent<- set_agent_info(project_info=project,driver_llm=driver_llm,input_data=hist_data,forecast_horizon=6)# iterate forecast via agentiterate_forecast(agent_info=agent,max_iter=3,weighted_mape_goal=0.03)# load final forecast outputforecast_output<- get_agent_forecast(agent_info=agent)# ask agent to explain the forecast resultseda_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?")

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