SAP HANA Predictive Analysis Library (PAL)

PAL_FORECASTACCURACYMEASURES_RESULT_TBL:
3.5.6 Forecast Smoothing
Forecast smoothing is used to calculate optimal parameters of a set of smoothing functions in PAL, including
Single Exponential Smoothing, Double Exponential Smoothing, and Triple Exponential Smoothing.
This function also outputs the forecasting results based on these optimal parameters. This optimization is
computed by exploring of the parameter space which includes all possible parameter combinations. The
quality assessment is done by comparing historic and forecast values. In PAL, MSE (mean squared error) or
MAPE (mean absolute percentage error) is used to evaluate the quality of the parameters.
The parameter optimization is based on global and local search algorithms. The global search algorithm used
in this function is simulated annealing, whereas the local search algorithm is Nelder Mead. Those algorithms
allow for efficient search processes.
To evaluate the flexibility of the function, a train-and-test scheme is carried out. In other words, a partition of
the time series is allowed, of which the former one is used to train the parameters, whereas the latter one is
applied to test.
The error of every forecast value is calculated as follows:
D i =(1+i* ∝ 2 )*E
Where ∝ is the weight of smoothing and E is the calculated MSE or MAPE value.
Prerequisites
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No missing or null data in the inputs.
The data is numeric, not categorical.
SAP HANA Predictive Analysis Library (PAL)
PAL Functions P U B L I C 369