SARIMA :: Kloudfuse Docs

SARIMA

SARIMA is the abbreviation for Seasonal Autoregressive Integrated Moving Average, a time series analysis in the fields of statistics and econometrics.

Without accounting for seasonality, we utilize three parameters:

p: Number of historical points considered for auto-regression (AR)
q: Number of historical points considered for moving averages (MA).
d: Number of times to apply differencing. Specifies that calculations should be made on the differences between consecutive points, rather than the raw points.

To make predictions, we maximize p and q historical points. This means that we use $max(p,q)+d$ historical points to make a prediction.

When considering seasonality, we add these additional parameters:

sp: Seasonally-adjusted number of historical points considered for auto-regression (AR)
sq: Seasonally-adjusted number of historical points considered for moving averages (MA).
sd: Seasonally-adjusted number of times to apply differencing.
sm: Number of discrete timestamps in a period.

In Kloudfuse, we implement the SARIMA algorithm as the agile option for anomaly detection.

In Dashboards

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

sarima( 
  ${promql}, 
  2, 1, 2, 0, 0, 0, 0, 
  ${bound}, 
  ${band} 
)


1	`${promql}`: PromQL query to evaluate
2	`${bound}`: Number of standard deviations (stdv): 1, 2, or 3
3	`${band}`: 4 = lower band, 5 = upper band, 6 = both upper and lower bands

Limitations

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

Next steps

For an in-depth discussion of the SARIMA functions, see these external resources:

Seasonal AutoRegressive Integrated Moving Average with eXogenous regressors model in StatsModels
Autoregressive integrated moving average in Wikipedia