# Forecast several columns at once

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You have more than one number to predict per series — sales and returns, for
example. Give each one the `"target"` kind and both are forecast in the same
request:

```json
{
  "input": {
    "source": {
      "inline": {
        "date":    ["2024-01-01", "2024-01-02", "2024-01-03"],
        "store":   ["S1", "S1", "S1"],
        "sales":   [142, 137, 145],
        "returns": [6, 4, 9]
      }
    },
    "columns": {
      "date":    { "kind": "time", "frequency": "1d" },
      "store":   "identifier",
      "sales":   { "kind": "target", "aggregate": "sum" },
      "returns": { "kind": "target", "aggregate": "sum" }
    }
  },
  "prediction_length": "2w",
  "quantiles": [0.1, 0.5, 0.9]
}
```

Post it to `https://api.retrocast.com/v1-beta/forecast?model=t0-alpha`.
`model` names the forecasting model and is required — without it the
request is rejected with `missing field "model"`.

There is no `targets` field in this request. Leave it out and every column
with the `"target"` kind is forecast.

## Forecast only some of them

To forecast a subset without editing the schema, list the columns you want in
`targets`:

```json
"targets": ["sales"]
```

The names must be columns you declared as targets. `returns` stays in the
data as history the model can use, but no forecast is returned for it.

## What comes back

`data.targets` is keyed by column name, one entry per forecast column. The
arrays below are trimmed to the first two of the 14 steps:

```json
{
  "data": {
    "time":      ["2024-01-04T00:00:00Z", "2024-01-05T00:00:00Z"],
    "cutoff":    ["2024-01-03T00:00:00Z", "2024-01-03T00:00:00Z"],
    "lead_time": ["PT0S", "P1D"],
    "span":      ["P1D", "P1D"],
    "identifiers": { "store": ["S1", "S1"] },
    "targets": {
      "sales":   { "quantiles": { "0.1": [137.7, 137.5], "0.5": [141.0, 140.5], "0.9": [147.4, 146.2] } },
      "returns": { "quantiles": { "0.1": [4.1, 4.1], "0.5": [6.2, 5.8], "0.9": [10.4, 9.5] } }
    }
  }
}
```

Every target shares one time axis and one set of levels: `prediction_length`,
`cutoff`, `lead_time` and `quantiles` are set once for the request and apply
to all of them. With `compute_metrics: true`, each entry in `metrics` is
keyed by target column the same way, so you get separate scores per column.