Review inference results - Amazon IoT SiteWise
Retrieve inference resultsUnderstand inference results
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Review inference results

Retrieve inference results

Latest inference results

Run the following command to fetch the most recent inference result for an asset property. For more information, see get-asset-property-value in the Amazon CLI Command Reference Guide.

aws iotsitewise get-asset-property-value \
    —asset-id asset-id \
    —property-id result-property-id

Inference results history

Run the following command to fetch the history of inference results for a specified time window. For more information, see get-asset-property-value-history in the Amazon CLI Command Reference Guide.

aws iotsitewise get-asset-property-value-history \
    —asset-id asset-id \
    —property-id result-property-id \
    —start-date start-time \
    —end-date end-time

Example response

Example of an inference result response:
{
  "value": {
    "stringValue": "{\"timestamp\": \"2025-02-10T22:42:00.000000\", \"prediction\": 0, \"prediction_reason\": \"NO_ANOMALY_DETECTED\", \"diagnostics\": [{\"name\": \"asset-id\\\\property-id\", \"value\": 0.53528}]}"
  },
  "timestamp": {
    "timeInSeconds": 1739227320,
    "offsetInNanos": 0
  },
  "quality": "GOOD"
}

Response fields

  • value.stringValue – A JSON string containing the inference result with the following fields:

    • timestamp – The timestamp of the TQV against which inference is performed.

    • prediction – The prediction result (0 for no anomaly, 1 for anomaly detected).

    • prediction_reason – The reason for the prediction (NO_ANOMALY_DETECTED or ANOMALY_DETECTED).

    • diagnostics – An array of diagnostic information showing contributing factors.

  • timestamp – The timestamp when the result is recorded in Amazon IoT SiteWise.

  • quality – The quality of the data point (typically GOOD).

Understand inference results

An inference result returned by Amazon IoT SiteWise anomaly detection includes key information about the model's prediction at a specific timestamp, including whether an anomaly was detected and which sensors contributed to the anomaly.

Example of a detailed inference result:
{
    "timestamp": "2021-03-11T22:25:00.000000",
    "prediction": 1,
    "prediction_reason": "ANOMALY_DETECTED",
    "anomaly_score": 0.72385,
    "diagnostics": [
      { "name": "asset_id_1\\\\property_id_1", "value": 0.02346 },
      { "name": "asset_id_2\\\\property_id_2", "value": 0.10011 },
      { "name": "asset_id_3\\\\property_id_3", "value": 0.11162 }
    ]
}

The diagnostics field is useful for interpreting why the model makes a certain prediction. Each entry in the list includes:

  • name: The sensor that contributed to the prediction (format: asset_id\\\\property_id).

  • value: A floating-point number between 0 and 1, representing the relative weight or importance, of that sensor at that point in time.

User benefits:

  • Understand which sensors had the strongest impact on an anomaly.

  • Correlate high-weight sensors with physical equipment behavior.

  • Inform root cause analysis.

Note

Even when prediction = 0 (normal behavior), the diagnostics list is returned. This helps assess which sensors are currently influencing the model's decisions, even in healthy states.