python 指數平滑簡單使用

# 平滑指數
def calc_next_s(alpha, x):
    s = [0 for  i in range(len(x))]
    s[0] = np.sum(x[0:3]) / float(3)
    for i in range(1, len(s)):
        s[i] = alpha*x[i] + (1-alpha)*s[i-1]
    return s

# 預測
def time_predict(x):
　　 alpha = 0.8
    s1 = calc_next_s(alpha, x)　　# 一次
    s2 = calc_next_s(alpha,s1)　　# 二次
    s3 = calc_next_s(alpha, s2)　　# 三次
    a3 = [(3 * s1[i] - 3 * s2[i] + s3[i]) for i in range(len(s3))]
    b3 = [((alpha / (2 * (1 - alpha) ** 2)) * ((6 - 5 * alpha) * s1[i] - 2 * (5 - 4 * alpha) * s2[i] + (4 - 3 * alpha) * s3[i])) for i in range(len(s3))]
    c3 = [(alpha ** 2 / (2 * (1 - alpha) ** 2) * (s1[i] - 2 * s2[i] + s3[i])) for i in range(len(s3))]
    pred = a3[-1]+b3[-1]*1+c3[-1]*(1**2)
    print(pred)

if __name__ == "__main__:
    x = []    # x內填寫需要預測的列表數據
    time_predict(x)