Forecast Value Added

Problem

Compute the value a forecast adds over a naive benchmark: naive_error - forecast_error.

Positive values mean the model beats the naive forecast; zero means it adds nothing.

Hint

This is the error gap in favor of the more sophisticated forecast.

case 1

in: [10,7]

expected: 3

case 2

in: [5,5]

expected: 0

case 3

in: [4,9]

expected: -5

case 4

in: [0,0]

expected: 0

Starter codePython

def forecast_value_added(naive_error, forecast_error):
    # Your code here
    pass

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