基于python的bi-lstm时间预测网络的实现

以下是一个基于Python的Bi-LSTM时间序列预测网络的简单实现，你可以根据自己的需求进行修改和优化：

# 导入所需库
import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense, LSTM, Bidirectional
from sklearn.preprocessing import MinMaxScaler

# 读入数据
data = pd.read_csv('data.csv', header=None)

# 数据预处理
scaler = MinMaxScaler(feature_range=(0, 1))
data = scaler.fit_transform(data)

# 将数据转换为有监督学习数据集
def create_dataset(dataset, look_back=1):
    X, Y = [], []
    for i in range(len(dataset)-look_back):
        X.append(dataset[i:(i+look_back), 0])
        Y.append(dataset[i + look_back, 0])
    return np.array(X), np.array(Y)

look_back = 5
X, Y = create_dataset(data, look_back)

# 划分训练集和测试集
train_size = int(len(X) * 0.67)
test_size = len(X) - train_size
train_X, train_Y = X[:train_size], Y[:train_size]
test_X, test_Y = X[train_size:], Y[train_size:]

# 调整输入数据的形状
train_X = np.reshape(train_X, (train_X.shape[0], train_X.shape[1], 1))
test_X = np.reshape(test_X, (test_X.shape[0], test_X.shape[1], 1))

# 构建模型
model = Sequential()
model.add(Bidirectional(LSTM(50, activation='relu'), input_shape=(look_back, 1)))
model.add(Dense(1))
model.compile(loss='mse', optimizer='adam')

# 训练模型
model.fit(train_X, train_Y, epochs=100, batch_size=1, verbose=2)

# 预测结果
train_predict = model.predict(train_X)
test_predict = model.predict(test_X)

# 反归一化
train_predict = scaler.inverse_transform(train_predict)
train_Y = scaler.inverse_transform([train_Y])
test_predict = scaler.inverse_transform(test_predict)
test_Y = scaler.inverse_transform([test_Y])

# 计算误差
train_score = np.sqrt(mean_squared_error(train_Y[0], train_predict[:,0]))
test_score = np.sqrt(mean_squared_error(test_Y[0], test_predict[:,0]))
print('Train Score: %.2f RMSE' % (train_score))
print('Test Score: %.2f RMSE' % (test_score))

以上代码中，我们首先读入数据，然后进行数据预处理和转换为有监督学习数据集。接着，我们将数据集划分为训练集和测试集，并使用双向LSTM模型进行训练和预测。最后，我们计算误差并输出结果。你可以根据自己的需求对代码进行修改和优化。