Forecast
Generate forward-looking predictions from a Digital Twin.
Runs a digital twin forward and emits the forecast, with the drivers behind it.
attribution is what separates this from a curve-fitter: the twin knows which variables moved the forecast and by how much, so the numbers can be explained rather than only plotted.
At a glance
Module type
ForecastSim
Category
Intelligence
Default tier
reactive
Runs on
reactive, durable
Starts a flow
no
Holds a body
no
Keeps state between runs
no
Available in
every application type
Parameters
digitalTwinId
string · required · defaults to ""
The twin to run against.
digitalTwinName
string · optional
Its name when it was chosen, for the node's label.
digitalTwinVersionIds
string[] · optional
Pin the run to specific twin versions; empty means the latest.
scenario
Record<string, unknown> · optional
The saved scenario. Anything arriving on the scenario port is merged over this.
scenarioDraft
Record<string, unknown> · optional
What the inspector is editing, before it is applied to scenario.
scenarioValidationErrors
string[] · optional
What was wrong with the draft the last time it was checked.
entityColumns
object · optional · defaults to {"id":null,"target":null,"constraints":null,"levers":null}
Which columns of the entities frame carry what: id the environment id, and target, constraints and levers the optional per-row overrides. Any may be null.
Inputs
on
event
no
Implicit on every module that does not itself start a flow: wire an event here to make this module the start of what that event runs.
scenario
json
no
Per-run overrides, merged over the saved scenario.
entities
dataframe
no
One row per environment to forecast; entityColumns maps its columns.
model
json
no
A twin version resolved upstream, instead of the one pinned in settings.
baseline
dataframe
no
History to forecast from; unconnected, the twin's own training data is used.
Outputs
forecast
dataframe
dataframe, signal
One row per version, environment, variable and step: timestamp, kind, prediction, lower, upper, std, confidence; a categorical target adds predictionLabel and probability.
attribution
dataframe
dataframe, signal
The same grain plus a component column — trend, seasonal, parents, delta and parent:<key> — carrying how much each contributed.
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