用Python语言编一个ARIMA-GARCH预测模型

好的，以下是一个简单的ARIMA-GARCH预测模型的Python代码：

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from arch import arch_model
from statsmodels.tsa.arima_model import ARIMA

# 读取数据
data = pd.read_csv('data.csv', index_col='Date', parse_dates=True)

# 将数据集按照时间顺序排序
data = data.sort_index()

# 拆分训练集和测试集
train_data = data[:'2018']
test_data = data['2019':]

# ARIMA模型拟合
model_arima = ARIMA(train_data, order=(2, 1, 2))
model_arima_fit = model_arima.fit(disp=0)

# 预测
arima_forecast = model_arima_fit.forecast(steps=12)[0]

# GARCH模型拟合
model_garch = arch_model(train_data, p=1, q=1)
model_garch_fit = model_garch.fit(disp='off')

# 预测方差
garch_variance = model_garch_fit.forecast(horizon=12).variance.values[-1, :]

# 组合预测结果
forecast = arima_forecast * np.sqrt(garch_variance)

# 绘制预测结果
plt.plot(test_data, label='Actual')
plt.plot(forecast, label='Forecast')
plt.legend()
plt.show()

其中，`data.csv`是你的数据集，`train_data`和`test_data`是训练集和测试集，`order`参数是ARIMA模型的阶数，`p`和`q`分别是GARCH模型的自回归项和滞后项数。预测结果是组合了ARIMA和GARCH模型的预测值。