vmd分解-gru的时间序列预测代码

以下是使用GRU进行时间序列预测的代码示例：

import pandas as pd
import numpy as np
from keras.models import Sequential
from keras.layers import Dense, GRU
from sklearn.preprocessing import MinMaxScaler

# 加载数据
data = pd.read_csv('data.csv')

# 将数据转换为numpy数组
data = data.values

# 将数据缩放到0-1之间
scaler = MinMaxScaler(feature_range=(0, 1))
data = scaler.fit_transform(data)

# 划分数据集
train_size = int(len(data) * 0.7)
test_size = len(data) - train_size
train, test = data[0:train_size, :], data[train_size:len(data), :]

# 创建数据集函数
def create_dataset(dataset, look_back=1):
    X, Y = [], []
    for i in range(len(dataset)-look_back-1):
        a = dataset[i:(i+look_back), 0]
        X.append(a)
        Y.append(dataset[i + look_back, 0])
    return np.array(X), np.array(Y)

# 创建训练集和测试集
look_back = 12
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)

# 调整输入形状
trainX = np.reshape(trainX, (trainX.shape[0], trainX.shape[1], 1))
testX = np.reshape(testX, (testX.shape[0], testX.shape[1], 1))

# 创建并拟合GRU模型
model = Sequential()
model.add(GRU(4, input_shape=(look_back, 1)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=100, batch_size=1, verbose=2)

# 在训练集和测试集上进行预测
trainPredict = model.predict(trainX)
testPredict = model.predict(testX)

# 反缩放预测值
trainPredict = scaler.inverse_transform(trainPredict)
trainY = scaler.inverse_transform([trainY])
testPredict = scaler.inverse_transform(testPredict)
testY = scaler.inverse_transform([testY])

# 计算均方根误差
trainScore = np.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))
print('Train Score: %.2f RMSE' % (trainScore))
testScore = np.sqrt(mean_squared_error(testY[0], testPredict[:,0]))
print('Test Score: %.2f RMSE' % (testScore))

在此示例中，我们使用了一个具有4个隐藏单位的GRU层，并使用均方误差作为损失函数。我们使用训练集来训练模型，并使用测试集来评估模型的性能。最后，我们将预测值反缩放回原始比例，并计算均方根误差以评估模型的性能。