AI Agent Capabilities

Source: vignettes/ai-agent.Rmd

Why an AI agent in finnts?

The AI agent is a tool-calling orchestration layer that sits on top of the core finnts pipeline. It uses an LLM to:

  • run lightweight EDA (missing data, outliers, (P)ACF, seasonality, stationarity),
  • select/adjust feature recipes (lags, rolling windows, Fourier, date feats),
  • choose model families (local vs global; ARIMA/ETS/LM/Tree/GBM/NN, etc.), backtests, and ensembling,
  • optionally detect and use hierarchies (bottoms-up, standard, grouped),
  • generate a reproducible best run and a consolidated forecast artifact you can pull with a single call.

You keep control through a few inputs (data, horizon, optional regressors, performance goal, iteration budget); the agent does the rest.

Core Agent Functions:

  • iterate_forecast(): Run the agent to iterate toward a best forecast run.
  • update_forecast(): Update forecasts with new data, using models trained in previous agent runs, optionally re-invoking the agent if accuracy degrades.
  • ask_agent(): Ask natural language questions about your forecast results and get data-driven answers.

What the agent produces

  • A versioned agent run (with agent_version and run_id) and log files.
  • A best-run table per time series with metrics.
  • A combined forecast table (optionally reconciled if hierarchy is used).
  • EDA artifacts (missing data, outliers, stationarity, seasonality, ACF/PACF).
  • Summaries of trained models (hyperparameters, recipes, feature importance).

Use these helpers to retrieve outputs:

  • get_best_agent_run(agent_info)
  • get_agent_forecast(agent_info)
  • ask_agent(agent_info, question)

Prerequisites

  • Package: finnts
  • LLM driver: the agent uses an ellmer Chat object for tool calling.
    • Example here shows Azure OpenAI, but any ellmer Chat backend works.

Set up environment variables for Azure OpenAI (example):

Sys.setenv(
  AZURE_OPENAI_ENDPOINT    = "<your-endpoint>",
  AZURE_OPENAI_API_KEY     = "<your-key>",
  AZURE_OPENAI_API_VERSION = "<api-version>"
)

End-to-end: first run with the AI agent

Below is a complete flow using the built-in M4 monthly sample.

1) Create a project 

library(finnts)
library(dplyr)

project <- set_project_info(
  project_name = "ai_agent_demo",
  path = tempdir(), # or a persistent folder
  combo_variables = c("id"),
  target_variable = "value",
  date_type = "month", # day|week|month|quarter|year
  fiscal_year_start = 1 # fiscal month (1 = Jan)
)

Tip: path controls where logs/forecasts/EDA artifacts are saved.
Supports local filesystem, Azure Blob (via AzureStor::blob_container), or Microsoft 365 drives (ms365r) via storage_object.

2) Bring data

  • One row per time series combo/date.
  • A single Date column named Date (class Date).
  • Target column matches target_variable.
  • Optional external regressors as extra columns (historical or historical+future values, if you want them used in forecasting).
hist_data <- timetk::m4_monthly %>%
  dplyr::filter(date >= as.Date("2013-01-01")) %>%
  dplyr::rename(Date = date) %>%
  dplyr::mutate(id = as.character(id))

3) Define the LLMs

  • driver_llm: executes the workflow via tool calling.
  • reason_llm (optional): does the chain-of-thought style reasoning / EDA digestion to choose Finn run inputs step-by-step.
driver_llm <- ellmer::chat_azure_openai(model = "gpt-4o-mini")
reason_llm <- ellmer::chat_azure_openai(model = "o4-mini") # can be the same or a more advanced reasoning model

4) Create the agent run 

agent <- set_agent_info(
  project_info = project,
  driver_llm = driver_llm,
  reason_llm = reason_llm, # optional but recommended
  input_data = hist_data,
  forecast_horizon = 6, # number of future periods
  external_regressors = NULL, # e.g., c("Price","Promo")
  allow_hierarchical_forecast = FALSE, # set TRUE to let agent use hierarchies
  overwrite = TRUE # start a fresh run_id if inputs changed
)

This writes the versioned inputs into path/input_data/ (hashed by combo/run) and logs the new agent_version/run_id.

5) Let the agent iterate to a best run 

iterate_forecast(
  agent_info          = agent,
  weighted_mape_goal  = 0.05, # your accuracy target of 5%
  max_iter            = 3, # stop after N iterations if not hitting goal
)

What happens under the hood:

  • Reads cached EDA (or computes it) via the agent’s EDA tools.
  • Chooses seasonal period, missing/outlier handling, box-cox/differencing strategy.
  • Sweeps models (local &/or global), backtests, recipes (lags/rolling/Fourier/date feats).
  • Optionally enables external regressors if they improve WMAPE.
  • Stops early if MWAPE goal met; otherwise iterates (up to max_iter).

6) Retrieve results 

best_runs <- get_best_agent_run(agent_info = agent, full_run_info = TRUE)
head(best_runs)

fcst <- get_agent_forecast(agent_info = agent)
head(fcst)
  • best_runs summarizes, for each time series combo, the best run inputs when calling the Finn forecast process.
  • fcst returns the consolidated forecast table (if hierarchical reconciliation was used, this is the reconciled output).

Ask questions about your forecast results

After running iterate_forecast() or update_forecast(), you can use ask_agent() to ask natural language questions about your results. The agent analyzes your forecast data, model configurations, and EDA outputs to provide data-driven answers.

How it works

ask_agent() creates an LLM-driven workflow that: 1. Plans the analysis steps needed to answer your question 2. Executes R code to analyze the relevant data 3. Generates a natural language answer based on the results

Example questions 

# Ask about forecast accuracy
answer <- ask_agent(
  agent_info = agent,
  question = "What is the average weighted MAPE across all time series?"
)

# Ask about models used
answer <- ask_agent(
  agent_info = agent,
  question = "Which models were selected as best for each time series?"
)

# Ask about feature importance
answer <- ask_agent(
  agent_info = agent,
  question = "What are the top 3 most important features for the forecast models?"
)

# Ask about data quality
answer <- ask_agent(
  agent_info = agent,
  question = "Were there any missing values or outliers in the data?"
)

# Ask about specific forecasts
answer <- ask_agent(
  agent_info = agent,
  question = "What are the forecasted values for M750 for the next 3 months?"
)

# Ask about time series characteristics
answer <- ask_agent(
  agent_info = agent,
  question = "Which time series show strong seasonality patterns?"
)

# Ask comparative questions
answer <- ask_agent(
  agent_info = agent,
  question = "Which time series have the highest forecast uncertainty?"
)

What data sources are available

ask_agent() has access to four main data sources:

  1. Forecast results (get_agent_forecast()): Future predictions, back-test results, model selections, confidence intervals
  2. Model configurations (get_best_agent_run()): Feature engineering settings, transformations applied, model hyperparameters
  3. EDA results (get_eda_data()): Time series characteristics, seasonality, stationarity tests, data quality metrics
  4. Model summaries (get_summarized_models()): Feature importance, model parameters, recipe details

The agent automatically determines which data sources to use based on your question.

Tips for effective questions

  • Be specific about what you want to know
  • Reference specific time series by their combo ID if needed
  • Ask about metrics, patterns, or comparisons
  • The agent can perform calculations and aggregations on the fly

Updating with new data (production loop)

When you have new input data, keep the same project and create a new agent run with updated input_data. Then call update_forecast():

# suppose you've appended more months to hist_data:
hist_data2 <- hist_data %>% dplyr::filter(Date <= as.Date("2016-06-01"))

agent2 <- set_agent_info(
  project_info = project,
  driver_llm = driver_llm,
  reason_llm = reason_llm,
  input_data = hist_data2,
  forecast_horizon = 6,
  overwrite = TRUE # required to create a new agent version when running update_forecast()
)

update_forecast(
  agent_info             = agent2,
  weighted_mape_goal     = 0.05,
  allow_iterate_forecast = TRUE, # if degradation detected, allow the agent to re-iterate
  max_iter               = 2 # cap re-iteration cost
)

updated_fcst <- get_agent_forecast(agent2)

# Ask questions about the updated forecast
answer <- ask_agent(
  agent_info = agent2,
  question = "Summarize the forecast accuracy."
)

What update_forecast() does:

  • Rebuilds global models first (if used), then updates local series that need it.
  • Compares WMAPE to a trailing baseline of previous runs. If >40% of series are >20% worse than the previous run WMAPE, and allow_iterate_forecast = TRUE, it will invoke the iterate loop (bounded by max_iter) to recover accuracy.
  • Re-runs reconciliation if hierarchy is in play.

Hierarchies (optional)

Set allow_hierarchical_forecast = TRUE in set_agent_info() to let the agent detect:

  • none → use bottoms_up (default),
  • standard hierarchy (e.g., Region → Country → SKU),
  • grouped hierarchy (crossed dimensions).

When the agent selects a hierarchy, it will: - train at the selected aggregate(s), - reconcile down to the bottom level, - produce a reconciled get_agent_forecast() output.

For background and manual control, see the “Hierarchical Forecasting” vignette.

External regressors (xregs)

If you pass external_regressors = c("Price","Promo", ...):

  • Provide historical or historical+future values (length ≥ horizon) in your input_data for the selected columns.
  • The agent will test which specific xregs help; if they don’t, it will disable them for that iteration.
  • See the “External Regressors” vignette for data prep details.

Parallelism knobs

  • parallel_processing = "local_machine" runs each time series in parallel across local cores.
  • parallel_processing = "spark" executes combos on an Azure Databricks/Synapse Spark cluster (see “Parallel Processing” vignette).
  • inner_parallel = TRUE parallelizes work inside a combo (useful when outer parallelism is NULL or "spark").
  • num_cores = NULL defaults to all cores minus one.

Reading artifacts directly (optional)

You normally won’t need this, but for audits:

  • Inputs: path/input_data/…
  • EDA: path/eda/…
  • Logs: path/logs/… (includes the hashed *-agent_run.csv and *-agent_best_run.* for each version)
  • Final Agent Outputs: path/final_output/…

Use the helpers first; dig into files only if you must.