We can use the predict command to compare original data against model predictions, using historical data. The -s flag saves data to the default data source, and facilitates data vizualization. The -a flag calculates a score to detect anomalies.

Output data points are saved to a new measurement prediction_nab_cpu_utilization_asg_misconfiguration_mean_value__5m and each point contains the following information:

  • mean_value: the predicted normal value named according to the JSON model definition
  • lower_mean_value: the minimum normal value with 99.7 percent confidence
  • upper_mean_value: the maximum normal value with 99.7 percent confidence
  • score: anomaly score in range [0.0, 100.0]
  • is_anomaly: flag the data point as abnormal or not
loudml predict nab_cpu_utilization_asg_misconfiguration_mean_value__5m -f now-30d -t now -s -a