Smoothing Weight Decay

Problem

Compute the weight that exponential smoothing assigns to an observation j steps in the past, which is alpha * (1 - alpha)^j. The weights decay geometrically as the lag grows.

Return 0.0 for a negative lag.

Hint

A larger alpha puts more weight on recent observations.

Test cases

case 1

in: [0.5,0]

expected: 0.5

case 2

in: [0.5,2]

expected: 0.125

case 3

in: [0.3,1]

expected: 0.21

case 4

in: [0.5,-1]

expected: 0

Starter codePython

def smoothing_weight(alpha, j):
    # Your code here
    pass

4 test cases · run your code against them in the editor