python三次指数平滑法预测代码

下面是使用Python编写的三次指数平滑法预测代码：

import numpy as np

def triple_exponential_smoothing(series, alpha, beta, gamma, n_preds):
    """
    三次指数平滑法预测函数

    参数：
    series：时间序列数据
    alpha：平滑系数
    beta：趋势系数
    gamma：季节性系数
    n_preds：预测步数

    返回：
    预测结果
    """

    result = []
    season_length = len(series) // 4
    initial_seasonal_components = np.array([series[i] - series[i - season_length] for i in range(season_length)])
    
    def smooth(series, alpha):
        smoothed = [series[0]]
        for i in range(1, len(series)):
            smoothed.append(alpha * series[i] + (1 - alpha) * smoothed[i - 1])
        return smoothed

    def triple_smooth(series, alpha, beta, gamma, season_length, n_preds):
        smooth_result = smooth(series, alpha)
        trend = smooth(smooth_result, beta)
        seasonal = [initial_seasonal_components[i % season_length] for i in range(len(series))]
        forecast = [smooth_result[-1] + trend[-1] + seasonal[-season_length]]

        for _ in range(n_preds - 1):
            next_smooth = alpha * (series[-1] - seasonal[-1]) + (1 - alpha) * (smooth_result[-1] + trend[-1])
            next_trend = beta * (next_smooth - smooth_result[-1]) + (1 - beta) * trend[-1]
            next_seasonal = gamma * (series[-1] - next_smooth) + (1 - gamma) * seasonal[-season_length]
            forecast.append(next_smooth + next_trend + next_seasonal)
            smooth_result.append(next_smooth)
            trend.append(next_trend)
            seasonal.append(next_seasonal)
            series.append(next_smooth + next_trend + next_seasonal)

        return forecast

    result = triple_smooth(series, alpha, beta, gamma, season_length, n_preds)
    return result

# 使用示例
series = [10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]
alpha = 0.5
beta = 0.4
gamma = 0.3
n_preds = 4

predicted_values = triple_exponential_smoothing(series, alpha, beta, gamma, n_preds)
print("预测结果：", predicted_values)

以上代码实现了三次指数平滑法的预测功能。在函数`triple_exponential_smoothing`中，通过指定的平滑系数alpha、趋势系数beta和季节性系数gamma，以及预测步数n_preds，对给定的时间序列数据进行预测。代码中的例子是一个简单的序列，可以通过修改series、alpha、beta、gamma和n_preds来适应不同的场景和需求。最后，打印出预测结果predicted_values。