如果有20个风能装机数量,想通过灰色预测模型后用ARIMA模型组合预测残差序列组合预测,python实现代码是什么
时间: 2023-12-11 07:05:34 浏览: 76
以下是Python实现代码:
```python
# 导入所需的库
import pandas as pd
from statsmodels.tsa.arima_model import ARIMA
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error
from gray_system import GM11
# 读取数据
data = pd.read_excel('data.xlsx')
# 使用灰色预测模型预测
model = GM11(data['装机数量'])
data['灰色预测值'] = model.predict(len(data), len(data))
# 计算残差
data['残差'] = data['装机数量'] - data['灰色预测值']
# 画出残差的时序图
plt.plot(data['残差'])
plt.show()
# 使用ARIMA模型对残差进行预测
model = ARIMA(data['残差'], order=(1, 1, 1))
results = model.fit(disp=-1)
data['ARIMA预测值'] = results.fittedvalues
# 组合预测结果
data['预测值'] = data['灰色预测值'] + data['ARIMA预测值']
# 计算预测误差
rmse = mean_squared_error(data['装机数量'], data['预测值']) ** 0.5
print('RMSE:', rmse)
```
需要注意的是,上述代码使用了一个名为`gray_system`的自定义库,其中包含了灰色预测模型的实现。如果您没有这个库,可以将以下代码放到同一目录下的`gray_system.py`文件中:
```python
import numpy as np
class GM11:
def __init__(self, data):
self.data = np.array(data)
def get_alfa(self):
return 0.5 * (np.max(self.data) + np.min(self.data)) / np.mean(self.data)
def get_GM(self, x_0):
X_1 = np.cumsum(x_0)
Z_1 = (X_1[:-1] + X_1[1:]) / 2.0
Z_1 = Z_1.reshape((len(Z_1), 1))
B = np.append(-Z_1, np.ones_like(Z_1), axis=1)
Yn = x_0[1:].reshape((len(x_0) - 1, 1))
[[a], [b]] = np.dot(np.dot(np.linalg.inv(np.dot(B.T, B)), B.T), Yn)
return (a, b)
def predict(self, start, end):
alfa = self.get_alfa()
x_0 = self.data.copy()
x_1 = np.zeros_like(x_0)
x_1[0] = x_0[0]
for i in range(1, len(x_0)):
x_1[i] = (x_0[0] - alfa * x_0[i - 1]) / (1 - alfa)
a, b = self.get_GM(x_1)
result = np.zeros(end - start + 1)
result[0] = x_0[0]
for i in range(1, end - start + 1):
result[i] = (result[i - 1] - b / a) * np.exp(-a) + b / a
return result
```
阅读全文