CNN做时间序列预测_使用Keras实现CNN+BiLSTM+Attention的多维(多变量)时间序列预测

使用CNN+BiLSTM+Attention的多维时间序列预测是一种常见的方法，以下是使用Keras实现的代码示例：

首先，导入所需的库：

import numpy as np
import pandas as pd
from keras.models import Model
from keras.layers import Input, Conv1D, MaxPooling1D, Flatten, Dense, Dropout, LSTM, Bidirectional, Permute, TimeDistributed, multiply, concatenate, Reshape
from keras.utils.vis_utils import plot_model
from sklearn.preprocessing import MinMaxScaler

然后，加载数据集并对其进行预处理：

# 加载数据集
df = pd.read_csv('data.csv', index_col=0)
dataset = df.values

# 将数据集归一化
scaler = MinMaxScaler(feature_range=(0, 1))
dataset_scaled = scaler.fit_transform(dataset)

# 将数据集转换为3D张量
def create_dataset(dataset, look_back):
    dataX, dataY = [], []
    for i in range(len(dataset) - look_back):
        a = dataset[i:(i + look_back), :]
        dataX.append(a)
        dataY.append(dataset[i + look_back, :])
    return np.array(dataX), np.array(dataY)

look_back = 12
train_size = int(len(dataset_scaled) * 0.67)
test_size = len(dataset_scaled) - train_size
train, test = dataset_scaled[0:train_size, :], dataset_scaled[train_size:len(dataset_scaled), :]
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)

接下来，定义CNN-BiLSTM-Attention模型：

def attention_block(inputs, time_steps):
    a = Permute((2, 1))(inputs)
    a = TimeDistributed(Dense(time_steps, activation='softmax'))(a)
    a = Lambda(lambda x: K.mean(x, axis=1), name='dim_reduction')(a)
    a = RepeatVector(128)(a)
    a_probs = Permute((2, 1), name='attention_vec')(a)
    output_attention_mul = multiply([inputs, a_probs], name='attention_mul')
    return output_attention_mul

def create_model(trainX):
    inputs = Input(shape=(trainX.shape[1], trainX.shape[2]))
    conv1 = Conv1D(filters=64, kernel_size=3, activation='relu')(inputs)
    pool1 = MaxPooling1D(pool_size=2)(conv1)
    bilstm1 = Bidirectional(LSTM(units=64, activation='relu', return_sequences=True))(pool1)
    attention1 = attention_block(bilstm1, trainX.shape[1])
    flatten = Flatten()(attention1)
    dense1 = Dense(units=32, activation='relu')(flatten)
    dropout1 = Dropout(0.2)(dense1)
    dense2 = Dense(units=trainY.shape[1], activation='linear')(dropout1)
    model = Model(inputs=inputs, outputs=dense2)
    return model

model = create_model(trainX)
plot_model(model, to_file='model.png', show_shapes=True, show_layer_names=True)

最后，训练和评估模型：

model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=100, batch_size=64)

trainScore = model.evaluate(trainX, trainY, verbose=0)
print('Train Score: %.2f MSE (%.2f RMSE)' % (trainScore, math.sqrt(trainScore)))
testScore = model.evaluate(testX, testY, verbose=0)
print('Test Score: %.2f MSE (%.2f RMSE)' % (testScore, math.sqrt(testScore)))

这样就可以使用CNN-BiLSTM-Attention模型预测多维时间序列数据了。