SAP HANA Predictive Analysis Library (PAL)

F 1 = a 0 + b 0
2. Calculation:
S t = αx t + (1 – α)S t–1
T t = αS t + (1 – α) T t–1
a t = 2S t – T t
F t+1 = a t + b t
For adaptive brown exponential smoothing, you need to update the parameter of α for every forecasting. The
following rules must be satisfied.
1. Initialization:
S 0 = x 0
T 0 = x 0
a 0 = 2S 0 – T 0
F 1 = a 0 + b 0
A 0 = M 0 = 0
α 1 = α 2 = α 3 = δ = 0.2
2. Calculation:
E t = x t – F t
A t = δE t + (1 – δ)A t–1
M t = δ|E t | + (1 – δ)M t–1
St = α t x t + (1 – α t )S t–1
T t = α t S t + (1 – α t )T t–1
a t = 2S t – T t
F t+1 = a t + b t
Where α, δ ∈(0,1) are two user specified parameters. The model can be viewed as two coupled single
exponential smoothing models, and thus forecast can be made by the following equation:
F T+m = a T + mb T
Note
F 0 is not defined because there is no estimation for the time slot 0. According to the definition, you can get
F 1 = a 0 + b 0 and so on.
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 355