h-step Error Growth sqrt(h)

Problem

Under a random walk, forecast error standard deviation grows with the square root of the horizon: sigma_h = sigma * sqrt(h).

Return 0.0 for h <= 0.

Hint

Variances add linearly with the horizon, so standard deviations grow with its square root.

case 1

in: [1,4]

expected: 2

case 2

in: [0.5,9]

expected: 1.5

case 3

in: [2,0]

expected: 0

case 4

in: [3,1]

expected: 3

Starter codePython

def error_growth_sqrt_h(sigma, h):
    # Your code here
    pass

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