> For the complete documentation index, see [llms.txt](https://docs.rootcause.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.rootcause.ai/more-details/applications/application-modules/forecastsim.md).

# 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

| Port       | Kind        | Required | Description                                                                                                                           |
| ---------- | ----------- | -------- | ------------------------------------------------------------------------------------------------------------------------------------- |
| `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

| Port          | Kind        | Connects to           | Description                                                                                                                                                                                       |
| ------------- | ----------- | --------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `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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