SAP HANA Predictive Analysis Library (PAL)

Once the trend and seasonality are determined, you can eventually obtain the de-trended and de-seasonalized
series, that is, the random. As illustrated before, the trend component is estimated as moving average
seasonal component, correspondingly, can be estimated as
. The
decomposition,
, additive
or,
decomposition.
, multiplicative
Consequently, the random component can be calculated straightforwardly.
Note that during the course of calculating the moving average, the element at t is determined by x(t
−q, ..., t+q), where d=2q or d=2q+1, d is the seasonality cycle. As a result, the trend and random series
are valid only within the time range between q and n−q. Should there be no seasonality, the random series is
just exactly the input time series.
Prerequisites
● No null data in the inputs. The time periods should be unique and equal sampling.
● The length of time series must be at least 1.
● The data type of time periods is integer. The data type of time series is integer or double.
SEASONALITYTEST
This function identifies the seasonality and calculates de-seasonalized series (random) of a time series.
Procedure Generation
CALL SYS.AFLLANG_WRAPPER_PROCEDURE_CREATE (‘AFLPAL’, ‘SEASONALITYTEST’,
‘’, '', );
SAP HANA Predictive Analysis Library (PAL)
PAL Functions P U B L I C 417