Question

我尝试在python中创建一个ARMA-GARCH模型，然后使用arch包。

但是在arch包中我找不到ARMA的平均模型。

我尝试使用ARX均值模型并让滞后= [1,1]，但摘要看起来不像ARMA模型。

此套餐是否包含ARMA均值模型？

Answer 1

我从Jason Brownlee博士那里学到了这项技术，他写了18余本与应用机器学习，数学和统计学有关的书：

为了在适当的时候给予应有的荣誉，我在此引用本材料所学到的知识：

引用参考书：

机器学习精通博士Jason Brownlee

https://machinelearningmastery.com/develop-arch-and-garch-models-for-time-series-forecasting-in-python/

感谢杰森（Jason）数小时的工作，毫无疑问，头痛和眼睛疲劳。您告诉我机器学习可以很有趣！

Python中的ARCH和GARCH模型

# create a simple white noise with increasing variance
from random import gauss
from random import seed
from matplotlib import pyplot

# seed pseudorandom number generator
seed(1)

# create dataset
data = [gauss(0, i*0.01) for i in range(0,100)]

# plot
pyplot.plot(data)
pyplot.show()

# create dataset
data = [gauss(0, i*0.01) for i in range(1,100+1)]

# check correlations of squared observations
from random import gauss
from random import seed
from matplotlib import pyplot
from statsmodels.graphics.tsaplots import plot_acf

# seed pseudorandom number generator
seed(1)

# create dataset
data = [gauss(0, i*0.01) for i in range(0,100)]

# square the dataset
squared_data = [x**2 for x in data]

# create acf plot
plot_acf(np.array(squared_data))
pyplot.show()

# split into train/test
n_test = 10
train, test = data[:-n_test], data[-n_test:]

# example of ARCH model
from random import gauss
from random import seed
from matplotlib import pyplot
from arch import arch_model

# seed pseudorandom number generator
seed(1)

# create dataset
data = [gauss(0, i*0.01) for i in range(0,100)]

# split into train/test
n_test = 10
train, test = data[:-n_test], data[-n_test:]

# define model
model = arch_model(train, mean='Zero', vol='ARCH', p=15)

# fit model
model_fit = model.fit()

# forecast the test set
yhat = model_fit.forecast(horizon=n_test)

# plot the actual variance
var = [i*0.01 for i in range(0,100)]
pyplot.plot(var[-n_test:])

# plot forecast variance
pyplot.plot(yhat.variance.values[-1, :])
pyplot.show()

# example of ARCH model
# seed pseudorandom number generator
seed(1)

# create dataset
data = [gauss(0, i*0.01) for i in range(0,100)]

# split into train/test
n_test = 10
train, test = data[:-n_test], data[-n_test:]

# define model
model = arch_model(train, mean='Zero', vol='GARCH', p=15, q=15)

# fit model
model_fit = model.fit()

# forecast the test set
yhat = model_fit.forecast(horizon=n_test)

# plot the actual variance
var = [i*0.01 for i in range(0,100)]
pyplot.plot(var[-n_test:])

# plot forecast variance
pyplot.plot(yhat.variance.values[-1, :])
pyplot.show()

# define model
model = arch_model(train, mean='Zero', vol='GARCH', p=15, q=15)

看到的结果非常相似，但是迭代次数却是原来的两倍多...

引用参考书：

机器学习精通博士Jason Brownlee

https://machinelearningmastery.com/develop-arch-and-garch-models-for-time-series-forecasting-in-python/

如何在python中拟合ARMA-GARCH模型

1 个答案: