def holt()

in afa/core.py [0:0]

• 17 lines of code
• 5 McCabe index (conditional complexity)

def holt(y, horiz, **kwargs):
    """
    """

    alpha = kwargs.get("alpha", 0.2)
    beta = kwargs.get("beta", 0.2)
    extra_periods = horiz-1
    
    # Initialization
    f = [np.nan] # First forecast is set to null value
    a = [y[0]] # First level defined as the first demand point
    b = [y[1]-y[0]] # First trend is computed as the difference between the two first demand poiny

    # Create all the m+1 forecast
    for t in range(1,len(y)):
        # Update forecast based on last level (a) and trend (b)
        f.append(a[t-1]+b[t-1])

        # Update the level based on the new data point
        a.append(alpha*y[t]+(1-alpha)*(a[t-1]+b[t-1]))

        # Update the trend based on the new data point
        b.append(beta*(a[t]-a[t-1])+(1-beta)*b[t-1])

    # Forecast for all extra months
    for t in range(extra_periods):
        # Update the forecast as the most up-to-date level + trend
        f.append(a[-1]+b[-1])
        # the level equals the forecast
        a.append(f[-1])
        # Update the trend as the previous trend
        b.append(b[-1])

#   yp = np.clip(np.exp(f[-horiz:]), 0, None).round(0)
    yp = np.array(f[-horiz:])

    return yp