SAP HANA Predictive Analysis Library (PAL)

3.3.5 Polynomial Regression
Polynomial regression is an approach to modeling the relationship between a scalar variable y and a variable
denoted X. In polynomial regression, data is modeled using polynomial functions, and unknown model
parameters are estimated from the data. Such models are called polynomial models.
In PAL, the implementation of exponential regression is to transform to linear regression and solve it:
y = β 0 + β 1 × x + β 2 × x 2 + … + β n × x n
Where β0…βn are parameters that need to be calculated.
Let x = x1’, x 2 = x2’, …, x n = xn’, and then
y’ = β0’ + β1 × x1 + β2 × x2 + … + βn × xn
So, y’ and x1…xn is a linear relationship and can be solved using the linear regression method.
The implementation also supports calculating the F value and R^2 to determine statistical significance.
Prerequisites
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No missing or null data in the inputs.
The data is numeric, not categorical.
Given the structure as Y and X1...Xn, there are more than n+1 records available for analysis.
POLYNOMIALREGRESSION
This is a polynomial regression function.
Procedure Generation
CALL SYS.AFLLANG_WRAPPER_PROCEDURE_CREATE (‘AFLPAL’, ‘POLYNOMIALREGRESSION’,
‘’, '', );
The signature table should contain the following records:
Table 208:
Position Schema Name Table Type Name Parameter Type
1 IN
2 IN
3
4
OUT
OUT
SAP HANA Predictive Analysis Library (PAL)
PAL Functions P U B L I C 277