三次指数平滑法预测代码

以下是Python实现的三次指数平滑法预测代码：

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# 读取数据
data = pd.read_csv("data.csv")

# 设定alpha、beta、gamma的值
alpha = 0.2
beta = 0.2
gamma = 0.2

# 初始化预测值和误差
data['s1'] = data['value']
data['s2'] = data['value']
data['s3'] = data['value']
data['e1'] = 0
data['e2'] = 0
data['e3'] = 0

# 预测未来6个月
n_predict = 6

# 三次指数平滑法预测
for i in range(n_predict):
    # 计算预测值和误差
    data.loc[len(data)] = [np.nan, alpha * data['value'].iloc[-1] + (1 - alpha) * data['s1'].iloc[-1], beta * (data['s1'].iloc[-1] - data['s2'].iloc[-1]) + (1 - beta) * data['s2'].iloc[-1], gamma * (data['s1'].iloc[-1] - 2 * data['s2'].iloc[-1] + data['s3'].iloc[-1]) + (1 - gamma) * data['s3'].iloc[-1], data['value'].iloc[-1] - data['s1'].iloc[-1], data['s1'].iloc[-1] - data['s2'].iloc[-1], data['s2'].iloc[-1] - data['s3'].iloc[-1]]
    # 更新s1、s2、s3的值
    data['s1'].iloc[-1] = alpha * data['value'].iloc[-1] + (1 - alpha) * data['s1'].iloc[-2]
    data['s2'].iloc[-1] = beta * (data['s1'].iloc[-1] - data['s2'].iloc[-2]) + (1 - beta) * data['s2'].iloc[-2]
    data['s3'].iloc[-1] = gamma * (data['s1'].iloc[-1] - 2 * data['s2'].iloc[-1] + data['s3'].iloc[-2]) + (1 - gamma) * data['s3'].iloc[-2]

# 绘制原始数据和预测数据图
plt.plot(data['value'], label='Original')
plt.plot(data['s1'], label='Triple Exponential Smoothing')
plt.plot(data['s2'], label='Triple Exponential Smoothing')
plt.plot(data['s3'], label='Triple Exponential Smoothing')
plt.plot(data['s1'].iloc[-n_predict:], label='Predict')
plt.legend()
plt.show()

其中，`data.csv`是包含历史数据的csv文件，`alpha`、`beta`、`gamma`是平滑系数，`n_predict`是需要预测的未来时间段长度。