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