This function updates the forecast agent with the latest data and inputs.
If new time series are detected in the data (up to 20\
with a floor of 10), simple forecasts are automatically created for them
using default local model inputs without LLM involvement. If the number
of new series exceeds the cap, an error directs the user to use
iterate_forecast() instead.
update_forecast(
agent_info,
weighted_mape_goal = 0.1,
allow_iterate_forecast = FALSE,
max_iter = 3,
parallel_processing = NULL,
inner_parallel = FALSE,
num_cores = NULL,
seed = 123
)Agent info from set_agent_info()
Weighted MAPE goal the agent is trying to achieve for each time series
Logical indicating if the forecast iteration should be allowed if poor performance is detected, meaning >40% of time series with >20% worse weighted MAPE than previous agent run
Numeric indicating the maximum number of iterations if iterate_forecast is ran
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.
Nothing
If individual time series fail during the global or local model update
process, they are automatically re-forecast using default local model
inputs (the same treatment as new time series). If more than 20\
existing series (with a floor of 10) fail to update, an error is raised
directing the user to use iterate_forecast() instead.
Reused forecasts are checked against original-scale prepared actuals
after refitting and retuning. Quality-rejected series receive one default
reforecast through the same path as new series, without LLM quality judgments.
Quality-only rejections do not count toward the ordinary execution-failure
limit; existing data/provider failures retain that limit. Replacement models
are evaluated using final_models() and cannot trigger an unbounded retry.
Reused fits do not call final_models(); their components must pass hard
eligibility and their selected combination must pass applicable quality checks
before any reconciliation, including after retuning. Global updates recover
each series' saved winning single model or average from existing source
forecasts, refit the union of required components, and preserve each selected
subset. Requested but unselected models are not added back to the average.
All globally selected series must reference one winning global iteration.
Mixed global iteration metadata is rejected before refitting rather than
split into multiple global updates. Different model subsets within that
single iteration and separately selected local winners remain supported.
Missing or ambiguous saved winners or selected fits require restoring the
original artifacts; the requested model list is not used as a fallback.
These are checks on newly generated predictions, not repeated assessments of
past iteration winners. A reused hierarchy that
is incomplete or contains a rejected node is not reconciled; its covered
current series follow the existing default-local forecast path. Existing
run logs record default acceptance or rejection for restart safety. The
selected mixture is then reconciled without post-reconciliation future
evaluation, whole-set replacement, or late quality-triggered refitting.
Legacy second-order differenced original targets without their own starting
values require regeneration of prepared data from the original input.
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
llm <- ellmer::chat_azure_openai(model = "gpt-4o-mini")
# set up agent info
agent_info <- set_agent_info(
project_info = project,
llm = llm,
input_data = hist_data,
forecast_horizon = 6,
hist_end_date = as.Date("2014-12-01")
)
# run the forecast iteration process
iterate_forecast(
agent_info = agent_info,
max_iter = 3,
weighted_mape_goal = 0.03
)
# update the forecast with latest data and inputs
agent_info <- set_agent_info(
project_info = project,
llm = llm,
input_data = hist_data,
forecast_horizon = 6,
hist_end_date = as.Date("2014-12-01"),
overwrite = TRUE # required to update the agent for latest data and inputs
)
update_forecast(
agent_info = agent_info,
weighted_mape_goal = 0.03
)
} # }