Forecast Bias

Problem

Compute the forecast bias as the mean signed error forecast - actual: (1/n) * sum(forecast[t] - actual[t]).

Return 0.0 for an empty actual list.

Hint

Positive bias means the forecast systematically overshoots.

case 1

in: [[10,12,14],[11,11,11]]

expected: -1

case 2

in: [[1,2],[2,1]]

expected: 0

case 3

in: [[5],[7]]

expected: 2

case 4

in: [[1,2,3],[3,3,3]]

expected: 1

Starter codePython

def forecast_bias(actual, forecast):
    # Your code here
    pass

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