SAP HANA Predictive Analysis Library (PAL)

Name Data Type Default Value Description Dependency
IGNORE_ZERO Integer 0 ● 0: Uses zero values
in the input
dataset when calculating
MPE or
MAPE.
● 1: Ignores zero
values in the input
dataset when calculating
MPE or
MAPE.
Only valid when MEAS­
URE_NAME is MPE or
MAPE.
EXPOST_FLAG Integer 1 ● 0: Does not output
the expost
forecast, and just
outputs the forecast
values.
● 1: Outputs the expost
forecast and
the forecast values.
PREDICTION_CONFI­
DENCE_1
Double 0.8 Prediction confidence
for interval 1
Only valid when the
upper and lower columns
are provided in
the result table.
PREDICTION_CONFI­
DENCE_2
Double 0.95 Prediction confidence
for interval 2
Only valid when the
upper and lower columns
are provided in
the result table.
Note
Cycle determines the seasonality within the time series data by considering the seasonal factor of a data
point t-CYCLE+1 in the forecast calculation of data point t+1 . Additionally, the algorithm of TESM takes an entire
CYCLE as the base to calculate the first forecasted value for data point CYCLE+1 . The value for CYCLE should
be within the range of 2 ≤ CYCLE ≤ entire number of data point/2.
For example, there is one year of weekly data (52 data points) as input time series. The value for CYCLE
should range within 2 ≤ CYCLE ≤ 26. If CYCLE is 4, we get the first forecast value for data point 5 (e.g. week
201205) which considers the seasonal factor of data point 1 (e.g. week 201201). The second forecast value
for data point 6 (e.g. week 201206) considers the seasonal factor of data point 2 (e.g. week 201202), etc. If
CYCLE is 2, then we get the first forecast value for data point 3 (e.g. week 201203) which considers the
seasonal factor of data point 1 (e.g. week 201201). The second forecast value for data point 4 (e.g. week
201204) considers the seasonal factor of data point 2 (e.g. week 201202), etc.
412 P U B L I C
SAP HANA Predictive Analysis Library (PAL)
PAL Functions