SARIMA(1,0,0)(2,1,2)[12] with drift-GARCH(1,1)模型预测代码(R语言)不使用“rugarch”包
时间: 2024-03-31 07:38:15 浏览: 146
SARIMA-LSTM混合模型预测时间序列(Python)
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以下是SARIMA(1,0,0)(2,1,2)[12] with drift-GARCH(1,1)模型预测的R语言代码,不使用“rugarch”包。
首先,导入所需的R包:
```R
library(forecast)
library(tseries)
```
接下来,读取时间序列数据并进行必要的预处理:
```R
data <- read.csv("data.csv") # 读取数据
ts_data <- ts(data[,2], frequency = 12) # 转换为时间序列数据,假设数据每12个时间点重复一次
train_data <- window(ts_data, end = c(2018,12)) # 选择训练集
test_data <- window(ts_data, start = c(2019,1)) # 选择测试集
```
然后,拟合SARIMA(1,0,0)(2,1,2)[12] with drift模型:
```R
model <- arima(train_data, order = c(1,0,0), seasonal = list(order = c(2,1,2), period = 12), include.drift = TRUE)
```
接着,计算残差序列并拟合GARCH(1,1)模型:
```R
resid <- residuals(model)
garch_model <- garch(resid, order = c(1,1))
```
最后,使用所得到的SARIMA-GARCH模型进行预测:
```R
forecast_length <- length(test_data)
sarima_forecast <- forecast(model, h = forecast_length)
garch_forecast <- predict(garch_model, n.ahead = forecast_length)$pred
final_forecast <- sarima_forecast$mean + garch_forecast
```
其中,`final_forecast`即为使用SARIMA(1,0,0)(2,1,2)[12] with drift-GARCH(1,1)模型预测的结果。
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