gru模型交通量预测MRE代码
时间: 2023-06-26 19:06:15 浏览: 31
这里是一个基于GRU模型的交通量预测MRE代码示例:
```python
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense, LSTM, GRU
from sklearn.metrics import mean_squared_error
# 读取数据
data = pd.read_csv('traffic_volume.csv')
data = data.iloc[:, 1:]
# 数据预处理
scaler = MinMaxScaler(feature_range=(0, 1))
data_scaled = scaler.fit_transform(data)
# 分割训练集和测试集
train_size = int(len(data_scaled) * 0.8)
test_size = len(data_scaled) - train_size
train, test = data_scaled[0:train_size,:], data_scaled[train_size:len(data_scaled),:]
# 将时间序列转换为监督学习问题
def create_dataset(dataset, look_back=1):
dataX, dataY = [], []
for i in range(len(dataset)-look_back-1):
a = dataset[i:(i+look_back), 0]
dataX.append(a)
dataY.append(dataset[i + look_back, 0])
return np.array(dataX), np.array(dataY)
look_back = 24
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)
# 调整输入数据的形状
trainX = np.reshape(trainX, (trainX.shape[0], trainX.shape[1], 1))
testX = np.reshape(testX, (testX.shape[0], testX.shape[1], 1))
# 构建GRU模型
model = Sequential()
model.add(GRU(units=50, return_sequences=True, input_shape=(trainX.shape[1], 1)))
model.add(GRU(units=50))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
# 训练模型
model.fit(trainX, trainY, epochs=50, batch_size=72, verbose=2)
# 预测测试集
testPredict = model.predict(testX)
testPredict = scaler.inverse_transform(testPredict)
testY = scaler.inverse_transform([testY])
# 计算MRE
mre = np.mean(np.abs((testY - testPredict) / testY)) * 100
print('MRE: %.2f%%' % mre)
```
这里使用了一个具有50个神经元的GRU模型来进行交通量预测,并且使用了均方误差(MSE)作为损失函数来训练模型。最后,计算了测试集上的MRE来评估模型的性能。