Prophet

Prophet forecasts time series data using an additive model with non-linear trends. We recommend it for time series that exhibit strong seasonal effects, and contain several cycles of historical data.

Kloudfuse implements the Prophet algorithm as the agile-robust option for Anomaly Detection. It supports hourly, daily, and weekly seasonality.

In Dashboards

To use Prophet operator in a dashboard, apply the following function:

prophet( \
  ${promql}, \ (1)
  ${seasonality} \ (2)
  ${bound}, \ (3)
  ${band} \ (4)
)
code
1 ${promql}: PromQL query to evaluate
2 ${seasonality} 0 = hourly, 1 = daily, 2 = weekly
3 ${band}: 4 = lower band, 5 = upper band, 6 = both upper and lower bands
4 ${bound}: Number of standard deviations (stdv): 1, 2, or 3

Limitations

If the evaluated metrics do not exhibit true seasonality, Prophet may create incorrect (invalid) alerts, or mask valid alerting conditions.

Next steps

For an in-depth discussion of the Prophet approach, see these resources: