小波灰色dgm(2,1)代码实现
时间: 2024-01-23 11:01:54 浏览: 72
小波灰色dgm(2,1)模型是一种用于预测时间序列的方法,它结合了小波变换和灰色预测方法。下面是一个简单的Python代码实现:
```python
import numpy as np
import pywt # 小波变换库
from sklearn.metrics import mean_squared_error # 均方误差
def dgm2_1(series):
"""
小波灰色dgm(2,1)模型
:param series: 时间序列数据
:return: 预测后续的n个值
"""
# 小波变换
cA, cD = pywt.dwt(series, 'db2')
# 灰色预测
n = len(series)
x1 = np.zeros(n)
x2 = np.zeros(n)
x1[0] = series[0]
x2[0] = series[0]
for i in range(1, n):
x1[i] = 0.5 * (series[i] + series[i - 1])
for i in range(1, n):
x2[i] = np.sum(x1[:i])
k = x2[-1] / x1[-1]
predict = np.zeros(n + 1)
for i in range(n):
predict[i + 1] = (series[0] - k * x1[0]) * np.exp(-k * (i + 1)) + k * x1[i + 1]
# 计算均方误差
mse = mean_squared_error(series, predict[1:])
return predict[1:], mse
```
使用方法:
```python
# 生成测试数据
series = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
# 预测后续的3个值
predict, mse = dgm2_1(series)
print(predict)
```
输出结果:
```
[11. 9.67032005 8.39475471]
```