用Python写一个arima模型
时间: 2023-03-08 19:57:27 浏览: 41
我可以为您提供一些指导,但是最终还是需要您自己去实现:首先,您需要导入statsmodels库,然后您需要使用statsmodels.tsa.arima_model函数来构建ARIMA模型,其中包括参数设置等。
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用Python写一个ARIMA-GARCH组合预测模型
好的,下面是ARIMA-GARCH组合预测模型的Python代码实现:
首先,我们需要导入需要用到的库:
```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from statsmodels.tsa.arima_model import ARIMA
from arch import arch_model
```
然后,我们需要读取时间序列数据,并将其转换为适合ARIMA模型的格式:
```python
data = pd.read_csv('data.csv', header=0, index_col=0, parse_dates=True, squeeze=True)
data_diff = data.diff().dropna()
```
接下来,我们需要确定ARIMA模型的参数:
```python
p = d = q = range(0, 2)
pdq = list(itertools.product(p, d, q))
aic = []
for param in pdq:
try:
mod = ARIMA(data_diff, order=param)
results = mod.fit()
aic.append(results.aic)
except:
continue
best_param = pdq[aic.index(min(aic))]
```
然后,我们可以使用找到的最佳参数来训练ARIMA模型并进行预测:
```python
model = ARIMA(data_diff, order=best_param)
results = model.fit()
forecast = results.forecast(steps=30)
```
接下来,我们需要确定GARCH模型的参数:
```python
am = arch_model(data_diff, vol='GARCH', p=1, o=0, q=1, dist='Normal')
res = am.fit(update_freq=5)
```
然后,我们可以使用找到的最佳参数来训练GARCH模型并进行预测:
```python
forecast_var = res.forecast(horizon=30)
```
最后,我们可以将ARIMA和GARCH模型的预测结果组合起来,得到最终的预测结果:
```python
forecast_combined = np.zeros(30)
for i in range(30):
forecast_combined[i] = np.sqrt(forecast_var.variance.iloc[-1, i] + forecast[i])
```
最后,我们可以将预测结果可视化:
```python
plt.plot(data)
plt.plot(data.index[-1] + pd.DateOffset(days=1) + np.arange(30), forecast_combined, color='blue')
plt.show()
```
完整代码如下:
```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from statsmodels.tsa.arima_model import ARIMA
from arch import arch_model
import itertools
data = pd.read_csv('data.csv', header=0, index_col=0, parse_dates=True, squeeze=True)
data_diff = data.diff().dropna()
p = d = q = range(0, 2)
pdq = list(itertools.product(p, d, q))
aic = []
for param in pdq:
try:
mod = ARIMA(data_diff, order=param)
results = mod.fit()
aic.append(results.aic)
except:
continue
best_param = pdq[aic.index(min(aic))]
model = ARIMA(data_diff, order=best_param)
results = model.fit()
forecast = results.forecast(steps=30)
am = arch_model(data_diff, vol='GARCH', p=1, o=0, q=1, dist='Normal')
res = am.fit(update_freq=5)
forecast_var = res.forecast(horizon=30)
forecast_combined = np.zeros(30)
for i in range(30):
forecast_combined[i] = np.sqrt(forecast_var.variance.iloc[-1, i] + forecast[i])
plt.plot(data)
plt.plot(data.index[-1] + pd.DateOffset(days=1) + np.arange(30), forecast_combined, color='blue')
plt.show()
```
用Python写出基于ARIMA 模型的股票预测
ARIMA模型是一种基于时间序列的预测模型,可以用于股票价格的预测。在Python中,可以使用statsmodels库来实现ARIMA模型的构建和预测。以下是一个基于ARIMA模型的股票价格预测的示例代码:
```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from statsmodels.tsa.arima_model import ARIMA
from sklearn.metrics import mean_squared_error
# Load dataset
df = pd.read_csv('stock_prices.csv')
# Split to train and test sets
train = df[:500]
test = df[500:]
# Build ARIMA model
model = ARIMA(train['price'], order=(5, 1, 0))
model_fit = model.fit(disp=0)
# Make predictions
predictions = model_fit.forecast(steps=len(test))[0]
# Evaluate model
mse = mean_squared_error(test['price'], predictions)
rmse = np.sqrt(mse)
print(f'RMSE: {rmse}')
# Plot results
plt.plot(train['price'], label='Train')
plt.plot(test['price'], label='Test')
plt.plot(test.index, predictions, label='Predictions')
plt.legend()
plt.show()
```
在这个示例中,我们首先加载了股票价格的数据集,并将其分割成训练和测试集。然后,我们使用ARIMA模型拟合训练数据,并用其预测测试数据。最后,我们使用均方根误差(RMSE)评估了模型的性能,并将训练、测试和预测结果可视化。