tcn-lstm 预测代码

TCN-LSTM（Temporal Convolutional Network with Long Short-Term Memory）是一种结合了一维卷积神经网络（TCN）和长短期记忆网络（LSTM）的结构，常用于序列数据预测任务。这种组合能够捕捉长期依赖并处理高维时间序列数据。以下是一个简化的TCN-LSTM预测代码示例，以Python语言和Keras库为例：

import tensorflow as tf
from keras.models import Model
from keras.layers import Input, LSTM, Conv1D, MaxPooling1D, Flatten

# 定义输入层
input_shape = (None, input_data.shape[1]) # 根据实际数据形状调整
inputs = Input(shape=input_shape)

# 使用TCN层
num_filters = 64
kernel_size = 5
dropout_rate = 0.2
tcn_layer = Conv1D(filters=num_filters,
                   kernel_size=kernel_size,
                   padding='causal',
                   activation='relu')(inputs)
tcn_layer = Dropout(dropout_rate)(tcn_layer)
tcn_layer = MaxPooling1D(pool_size=2)(tcn_layer) # 可以选择其他的下采样策略

# 添加LSTM层
lstm_units = 128
lstm_layer = LSTM(units=lstm_units,
                  return_sequences=True)(tcn_layer)

# 全连接层或扁平化层，视情况而定
flatten_layer = Flatten()(lstm_layer)
output = Dense(units=num_classes, activation='softmax')(flatten_layer)

# 创建模型
model = Model(inputs=inputs, outputs=output)

# 编译模型
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# 训练模型
model.fit(x_train, y_train, validation_data=(x_val, y_val), epochs=epochs)

# 使用模型进行预测
predictions = model.predict(new_data)