R/agent_iterate_forecast.R
iterate_forecast.RdThis function orchestrates the forecast iteration process for a Finn agent, including exploratory data analysis,
iterate_forecast(
agent_info,
max_iter = 3,
weighted_mape_goal = 0.03,
parallel_processing = NULL,
inner_parallel = FALSE,
num_cores = NULL,
seed = 123
)Agent info from set_agent_info()
Maximum number of iterations for forecast optimization.
Weighted MAPE goal the agent is trying to achieve for each time series
Default of NULL runs no parallel processing and forecasts each individual time series one after another. 'local_machine' leverages all cores on current machine Finn is running on. 'spark' runs time series in parallel on a spark cluster in Azure Databricks or Azure Synapse.
Run components of forecast process inside a specific time series in parallel. Can only be used if parallel_processing is set to NULL or 'spark'.
Number of cores to run when parallel processing is set up. Used when running parallel computations on local machine or within Azure. Default of NULL uses total amount of cores on machine minus one. Can't be greater than number of cores on machine minus 1.
Set seed for random number generator. Numeric value.
if (FALSE) { # \dontrun{
# load example data
hist_data <- timetk::m4_monthly %>%
dplyr::filter(date >= "2013-01-01") %>%
dplyr::rename(Date = date) %>%
dplyr::mutate(id = as.character(id))
# set up Finn project
project <- set_project_info(
project_name = "Demo_Project",
combo_variables = c("id"),
target_variable = "value",
date_type = "month"
)
# set up LLM
driver_llm <- ellmer::chat_azure_openai(model = "gpt-4o-mini")
# set up agent info
agent_info <- set_agent_info(
project_info = project,
driver_llm = driver_llm,
input_data = hist_data,
forecast_horizon = 6
)
# run the forecast iteration process
iterate_forecast(
agent_info = agent_info,
max_iter = 3,
weighted_mape_goal = 0.03
)
} # }