CAViaR模型python代码
时间: 2023-06-13 16:03:53 浏览: 3953
CAViaR是一种深度学习模型,它可以用于时间序列预测。以下是使用Python编写的CAViaR模型代码示例:
首先,需要安装必要的库:
```python
!pip install keras
!pip install tensorflow
```
导入必要的库:
```python
import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers import Dropout
```
读取数据集:
```python
data = pd.read_csv("data.csv")
```
将数据集转换为numpy数组:
```python
data = data.values
```
将数据集分为训练集和测试集:
```python
train_size = int(len(data) * 0.67)
test_size = len(data) - train_size
train, test = data[0:train_size,:], data[train_size:len(data),:]
```
将数据集转换为CAViaR可接受的格式:
```python
def create_dataset(dataset, look_back=1):
X, Y = [], []
for i in range(len(dataset)-look_back-1):
X.append(dataset[i:(i+look_back), 0])
Y.append(dataset[i + look_back, 0])
return np.array(X), np.array(Y)
look_back = 1
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)
```
创建和训练CAViaR模型:
```python
model = Sequential()
model.add(LSTM(4, input_shape=(1, look_back)))
model.add(Dropout(0.2))
model.add(Dense(1))
model.compile(loss='mae', optimizer='adam')
model.fit(trainX, trainY, epochs=100, batch_size=32, verbose=2)
```
使用测试集计算模型的误差:
```python
testPredict = model.predict(testX)
```
计算误差:
```python
from sklearn.metrics import mean_absolute_error
mae = mean_absolute_error(testY, testPredict)
print("MAE: ", mae)
```
这就是一个简单的CAViaR模型的Python代码示例。