A First Example: Abnormal Dips in User Trafficedit

The easiest time series models have a unique feature. Past values are then used as a proxy to forecast future values. This unique feature is an io ie, input and output at the same time.

The following user_traffic.json file (Or .yml file if YAML format is used) is using:

  • One minute aggregations, ie bucket_interval equals 1m
  • A window size, ie the span equal to 10 bucket_interval ie 10 minutes
  • A low anomaly type, since we want to detect dips in user traffic
  "bucket_interval": "1m",
  "default_bucket": "my-bucket",
  "features": [
      "default": 0,
      "metric": "count",
      "field": "requests",
      "name": "count_all_requests",
      "anomaly_type": "low"
  "interval": 60,
  "max_evals": 10,
  "name": "traffic-model",
  "offset": 30,
  "span": 20,
  "max_threshold": 90,
  "min_threshold": 50,
  "type": "donut"