build_forecasting_agent: Build a Time Series Forecasting Agent

In LLMAgentR: Language Model Agents in R for AI Workflows and Research

Build a Time Series Forecasting Agent

Description

Constructs a state graph-based forecasting agent that: recommends forecasting steps, extracts parameters, generates code, executes the forecast using 'modeltime', fixes errors if needed, and explains the result. It leverages multiple models including Prophet, XGBoost, Random Forest, SVM, and Prophet Boost, and combines them in an ensemble.

Arguments

model	A function that takes a prompt and returns an LLM-generated result.
bypass_recommended_steps	Logical; skip initial step recommendation.
bypass_explain_code	Logical; skip the final explanation step.
mode	Visualization mode for forecast plots. One of '"light"' or '"dark"'.
line_width	Line width used in plotly forecast visualization.
verbose	Logical; whether to print progress messages.

Value

A callable agent function that mutates the given 'state' list.

Examples

## Not run: 
# 2) Prepare the dataset
my_data <- walmart_sales_weekly

# 3) Create the forecasting agent
forecasting_agent <- build_forecasting_agent(
  model = my_llm_wrapper,
  bypass_recommended_steps = FALSE,
  bypass_explain_code = FALSE,
  mode = "dark", # dark or light
  line_width = 3,
  verbose = FALSE
)

# 4) Define the initial state
initial_state <- list(
  user_instructions = "Forecast sales for the next 30 days, using `id` as the grouping variable,
  a forecasting horizon of 30, and a confidence level of 90%.",
  data_raw = my_data
)

# 5) Run the agent
final_state <- forecasting_agent(initial_state)

## End(Not run)

LLMAgentR documentation built on June 8, 2025, 10:02 a.m.