Model Inputs

Every model receives its input data as files, served to the Docker container via environment variables. Each input you define in your .pd4castrrc.json becomes an INPUT_<KEY>_URL environment variable that your model must fetch with a standard HTTP GET request. Inputs can be uploaded manually (static) at publish time or fetched automatically from external data sources (fetched).

How Inputs Work

When a model run starts, the platform makes each input file available at a unique URL. Your model must read these URLs from environment variables and download the data. The environment variable name is derived from the input’s key field, converted to uppercase. For example, an input with "key": "demand" becomes INPUT_DEMAND_URL.

Your model code fetches the file from that URL, parses it, and uses it as input data for your forecasting logic.

Input Configuration Fields

Each entry in the inputs array in .pd4castrrc.json supports the following fields.

Static Inputs

Static inputs are files you can upload automatically at publish time. They’re ideal for reference data, lookup tables, or configuration files that don’t change with every model run.

Static input files live in your project’s test_input/ directory. When you run pd4castr publish, the CLI uploads these files to the configured input source.

A minimal static input configuration looks like this:

{
  "key": "reference_prices",
  "trigger": "USE_MOST_RECENT_FILE",
  "uploadFileFormat": "csv",
  "targetFileFormat": "csv"
}

If you want to upload static input data outside of the publish flow to trigger a model run, you can contact us about having access to your own input source.

Dynamic Inputs (Data Fetchers)

Dynamic inputs pull data automatically from external sources on a schedule. This is useful for models that need fresh data without manual intervention.

By default, pd4castr provides an AEMO MMS data fetcher, which facilitates building forecasting models that consume Australian energy market data.

pd4castr supports integration with custom data sources. We can adapt to your preferred technology or process. To learn more,
contact us to connect your data source.

To configure a dynamic input, add a fetcher block to the input entry:

{
  "key": "predispatch_price",
  "trigger": "WAIT_FOR_LATEST_FILE",
  "uploadFileFormat": "json",
  "targetFileFormat": "json",
  "fetcher": {
    "type": "AEMO_MMS",
    "checkInterval": 300,
    "config": {
      "checkQuery": "queries/data-fetchers/predispatch-price-check.sql",
      "fetchQuery": "queries/data-fetchers/predispatch-price-fetch.sql"
    }
  }
}

The fetcher fields are:

The platform runs the check query at the configured interval. When the results differ from the previous check, it executes the fetch query and writes the output to storage. This new data can then trigger an automatic model run.

Trigger Types and Run Modes

Each input has a trigger that controls how it interacts with automatic model runs:

• WAIT_FOR_LATEST_FILE — The platform waits until this input has received new data before triggering a run. All inputs with this trigger type must have fresh data before the run starts.
• USE_MOST_RECENT_FILE — The platform uses whatever data was last available for this input. It doesn’t block a run from starting.

For models with runMode set to AUTOMATIC, the platform monitors all inputs. A run is triggered only when every WAIT_FOR_LATEST_FILE input has received new data. USE_MOST_RECENT_FILE inputs are included in the run using their most recently available file.

Example Configuration

Here’s a complete inputs array with both a static and a fetched input:

{
  "inputs": [
    {
      "key": "dispatch_price",
      "trigger": "WAIT_FOR_LATEST_FILE",
      "uploadFileFormat": "json",
      "targetFileFormat": "json",
      "fetcher": {
        "type": "AEMO_MMS",
        "checkInterval": 300,
        "config": {
          "checkQuery": "queries/data-fetchers/dispatch-price-check.sql",
          "fetchQuery": "queries/data-fetchers/dispatch-price-fetch.sql"
        }
      }
    },
    {
      "key": "regional_historical_prices",
      "trigger": "USE_MOST_RECENT_FILE",
      "uploadFileFormat": "csv",
      "targetFileFormat": "csv"
    }
  ]
}

In this example, dispatch_price is fetched automatically from AEMO every 5 minutes. regional_historical_prices is a static CSV file uploaded during publish. The model won’t run automatically until fresh dispatch price data arrives, but it uses the latest available regional historical prices data.

Next Steps

• Use pd4castr fetch to pull live data from your configured fetchers into your local test_input/ directory.
• See the Configuration File reference for the full schema.