VAR(1) Forecast

Problem

Forecast one step of a VAR(1) model: x_next[i] = c[i] + sum over j of A[i][j] * x_last[j], where c is the intercept vector, A is the coefficient matrix, and x_last is the current state vector.

Return the forecast vector.

Hint

Each output component is a dot product of one matrix row with the state plus its intercept.

Test cases

case 1

in: [[0.5,1],[[0.5,0.1],[0.2,0.3]],[2,3]]

expected: [1.8,2.3]

case 2

in: [[0,0],[[1,0],[0,1]],[2,3]]

expected: [2,3]

case 3

in: [[1],[[0.5]],[4]]

expected: [3]

case 4

in: [[0,0,0],[[1,2,3],[4,5,6],[7,8,9]],[1,0,-1]]

expected: [-2,-2,-2]

Starter codePython

def var1_forecast(c, A, x_last):
    # Your code here
    pass

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