二次移动平均法交通量预测RMSE代码
时间: 2023-10-03 07:04:53 浏览: 38
下面是使用二次移动平均法进行交通量预测的Python代码,并计算预测结果与实际值之间的均方根误差(RMSE)。
```python
import numpy as np
import pandas as pd
from sklearn.metrics import mean_squared_error
# 读取数据
data = pd.read_csv('traffic_volume.csv', index_col=0, parse_dates=True)
# 构造二次移动平均预测模型
def quadratic_moving_average(series, window_size):
half_window_size = window_size // 2
predictions = []
for i in range(half_window_size, len(series) - half_window_size):
window = series[i - half_window_size:i + half_window_size + 1]
prediction = 2 * np.mean(window) - np.mean(series[i - window_size:i])
predictions.append(prediction)
return predictions
# 预测
window_size = 24
predictions = quadratic_moving_average(data['volume'], window_size)
# 计算RMSE
actual = data['volume'][window_size:]
rmse = mean_squared_error(actual, predictions, squared=False)
print('RMSE:', rmse)
```
其中,`data`是包含交通量数据的DataFrame对象,`quadratic_moving_average`函数实现了二次移动平均预测模型,`window_size`是移动窗口的大小,`predictions`是预测结果,`actual`是实际值,`rmse`是计算得到的均方根误差。