Model Outputs

Your model produces forecast data by writing an output file to the platform. During a model run, the container uploads its results to the URL specified by the OUTPUT_URL environment variable using an HTTP PUT request. The platform processes this file, stores the forecast data, and makes it available in pdView.

How output works

When your container finishes its forecast computation, it must upload the result to the OUTPUT_URL environment variable. This is a standard HTTP PUT request with the output file as the request body.

The output file format is configured at the model level using the outputFileFormat field in .pd4castrrc.json. Supported formats are json, csv, and parquet. If you don’t specify a format, JSON is used by default.

Required columns

Every output file must contain a forecast_datetime column. This column holds the datetime values for each forecast data point in the output. All other columns are defined by the model author in the outputs array.

Output column configuration

The outputs array in .pd4castrrc.json defines the schema of your output data. Each entry describes one column in the output file.

FieldTypeRequiredDescription
namestringYesColumn name in the output data. Must include forecast_datetime.
typestringYesData type: float, integer, string, date, boolean, or unknown.
seriesKeybooleanYesWhether this column is a categorical series key. Series keys split data into separate chart lines.
colourstringNoHex colour code (#RRGGBB) for this series in the forecast chart.

Series keys

Columns marked with "seriesKey": true act as categorical identifiers that determine how the data is split into chart series in pdView. For example, a price forecast model covering multiple energy regions might define each region (NSW1, QLD1, SA1) as a series key. Each unique value in a series key column becomes a separate line on the forecast chart.

Columns with "seriesKey": false contain the actual forecast values (for example, the predicted price for each region at each datetime).

How outputs appear in the platform

Output columns map directly to what users see in pdView:

  • Series key columns create separate lines or categories on the forecast chart. Each unique value gets its own line with the specified colour.
  • Value columns provide the data points plotted on the chart.
  • The forecast chart displays data over time, using forecast_datetime as the x-axis.

Example

Here’s a complete outputs configuration for a price forecast model that covers five Australian energy regions:

{
  "outputs": [
    {
      "name": "forecast_datetime",
      "type": "date",
      "seriesKey": false
    },
    {
      "name": "NSW1",
      "type": "float",
      "seriesKey": true,
      "colour": "#84EDDC"
    },
    {
      "name": "QLD1",
      "type": "float",
      "seriesKey": true,
      "colour": "#FD4E4E"
    },
    {
      "name": "SA1",
      "type": "float",
      "seriesKey": true,
      "colour": "#FED600"
    },
    {
      "name": "TAS1",
      "type": "float",
      "seriesKey": true,
      "colour": "#40A967"
    },
    {
      "name": "VIC1",
      "type": "float",
      "seriesKey": true,
      "colour": "#1965C6"
    }
  ]
}

In this example, each region column is a series key with a distinct colour. pdView renders five separate lines on the forecast chart, one per region, each in the specified colour.

Next steps

  • See the Configuration file reference for all output field details.
  • Learn about Sensitivities and scenarios to run alternative versions of your model with modified inputs.