arima+garch生成代码

下面是使用Python中的statsmodels库实现ARIMA+GARCH模型的代码示例：

import pandas as pd
from statsmodels.tsa.arima_model import ARIMA
from statsmodels.tsa.arima_model import ARIMAResults
from statsmodels.tsa.stattools import acf, pacf
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
from statsmodels.tsa.stattools import adfuller
from statsmodels.tsa.statespace.sarimax import SARIMAX
from statsmodels.tsa.arima_process import ArmaProcess
from arch import arch_model
import matplotlib.pyplot as plt

# 加载时间序列数据
df = pd.read_csv('data.csv', index_col='date', parse_dates=True)

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

# ARIMA模型参数选择
# 确定d值
diff = 0
adf_result = adfuller(train_data['value'])
while adf_result[1] >= 0.05:
    diff += 1
    adf_result = adfuller(train_data['value'].diff(diff).dropna())

# 确定p和q值
lag_acf = acf(train_data['value'].diff(diff).dropna(), nlags=20, fft=False)
lag_pacf = pacf(train_data['value'].diff(diff).dropna(), nlags=20, method='ols')

# 绘制ACF和PACF图形
plt.figure()
plt.subplot(211)
plot_acf(train_data['value'].diff(diff).dropna(), ax=plt.gca(), lags=20)
plt.subplot(212)
plot_pacf(train_data['value'].diff(diff).dropna(), ax=plt.gca(), lags=20)
plt.show()

# ARIMA模型训练和预测
model_arima = ARIMA(train_data['value'].diff(diff).dropna(), order=(1, diff, 1))
model_arima_fit = model_arima.fit()
print(model_arima_fit.summary())
predict_arima = model_arima_fit.predict(start='2019-01-01', end='2019-12-01', typ='levels')

# GARCH模型训练和预测
model_garch = arch_model(train_data['value'], p=1, q=1)
model_garch_fit = model_garch.fit()
print(model_garch_fit.summary())
predict_garch = model_garch_fit.forecast(start='2019-01-01', horizon=12)

# 结合ARIMA和GARCH的预测结果
predict = predict_arima + predict_garch.mean.iloc[-1]
test_data['predict'] = predict
test_data[['value', 'predict']].plot(figsize=(12, 6))
plt.show()

以上代码首先加载时间序列数据，并将其拆分为训练集和测试集。然后使用ADF检验确定差分阶数，使用ACF和PACF图形确定ARIMA模型的p和q值。接下来训练ARIMA模型，并使用GARCH模型训练波动率。最后，将ARIMA和GARCH模型的预测结果相结合，生成最终的预测结果，并将其与测试集数据一起绘制在图表中。