Model guide

AutoARIMA

An automated ARIMA selection approach that searches across candidate configurations and uses information criteria to find a reasonable time-series fit. Useful when you want the convenience of ARIMA-style forecasting without manually tuning the order parameters first. A good starting point for teams that want a practical automatic baseline before hand-selecting a more specific time-series setup.

Model ID: timeseries/autoarima Time-series forecasting Trust-aware output guidance

Where to start

Use this guide to connect the route, request, and interpretation together

This page is focused on the practical path for AutoARIMA: which prediction routes are available, what the request body should communicate, what the response shape means, and how to test the route without exposing service credentials to browser clients.

Good fit

Use this model when historical signal behavior is meaningful enough to forecast the next time window.

Integration guidance

Server-side applications can call eazePredict routes with service credentials. Keep credentials out of browser clients and use your application layer to expose application-facing workflows.

Available endpoints

Verified route paths for AutoARIMA

These are the verified paths for this model family. Use the base URL /trace/engine/v1 before the route path in API mode.

POST /timeseries/autoarima

Runs the main model or analysis workflow for this guide.

Relative URL: /trace/engine/v1/timeseries/autoarima

Example request

Request shape for AutoARIMA

This example shows the core JSON shape expected by this guide. In deployed service-to-service calls, include customerKey for protected train, predict, and model-forecast routes, and keep service credentials outside browser clients.

  • timeSeriesFeatureModel identifies the saved time-series model family.
  • timeSeriesInput carries the historical signal values to fit or forecast.
  • forecast, timeSeriesType, and timeSeriesFrequency define the forecast horizon and cadence.
JSON request body 
{
  "customerKey": "eazepredict-demo",
  "forecast": 3,
  "startDate": "Wed, 01 Jan 2025 00:00:00 GMT",
  "timeSeriesFeatureModel": "productSalesForecast",
  "timeSeriesFrequency": 1,
  "timeSeriesInput": {
    "label": "units_sold",
    "output": [
      120,
      132,
      128,
      145,
      160,
      166,
      172,
      181,
      190,
      198
    ]
  },
  "timeSeriesType": "d",
  "train": 100
}

Example response

Successful response shape

Responses are JSON and vary by route. Training responses can include artifact metadata and trust summaries. Prediction and forecasting responses return rows keyed by timestamp, signal labels, model output, and any trust-aware warning fields produced by eazePredict.

  • result usually contains model fit, forecast, or analysis rows.
  • prediction appears on saved-model prediction routes.
  • error appears when validation, artifacts, or processing fail.
JSON response body 
{
  "message": "Models trained and saved successfully",
  "result": [
    {
      "timestamp": "Wed, 01 Jan 2025 10:00:00 GMT",
      "type": "actual",
      "units_sold": 120
    },
    {
      "timestamp": "Wed, 01 Jan 2025 12:00:00 GMT",
      "type": "actual",
      "units_sold": 132
    },
    {
      "timestamp": "Wed, 01 Jan 2025 14:00:00 GMT",
      "type": "actual",
      "units_sold": 128
    },
    {
      "timestamp": "Wed, 01 Jan 2025 16:00:00 GMT",
      "type": "actual",
      "units_sold": 145
    },
    {
      "timestamp": "Wed, 01 Jan 2025 18:00:00 GMT",
      "type": "actual",
      "units_sold": 160
    },
    {
      "timestamp": "Wed, 01 Jan 2025 20:00:00 GMT",
      "type": "actual",
      "units_sold": 166
    }
  ]
}

Example cURL

Test the route from a server-side app or terminal

This example uses the verified relative path selected for this model guide. Replace the token, deployment host, and request values with deployment-specific values.

cURL example 
curl -X POST "/trace/engine/v1/timeseries/autoarima" \
  -H "Authorization: Basic <aiml-service-token>" \
  -H "Content-Type: application/json" \
  -d '{"customerKey": "eazepredict-demo", "forecast": 3, "startDate": "Wed, 01 Jan 2025 00:00:00 GMT", "timeSeriesFeatureModel": "productSalesForecast", "timeSeriesFrequency": 1, "timeSeriesInput": {"label": "units_sold", "output": [120, 132, 128, 145, 160, 166, 172, 181, 190, 198]}, "timeSeriesType": "d", "train": 100}'

Trust behavior

How to interpret trust signals from this model

Numeric output alone should not drive operational action. Use trust-aware fields and context checks to decide whether to escalate, review, compare, or suppress.

Model disagreement

If multiple models produce materially different outputs, treat the result as uncertain and review the operating context.

Unseen regime

If the request falls far outside historical feature ranges or known operating combinations, the prediction may be unreliable.

Suppression behavior

Responses may return null plus a trust_warning instead of a misleading number when unsafe conditions are detected.

Back to model guides API documentation