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import torch
import torch.nn as nn
class TCN(nn.Module):
def __init__(self):
super(TCN, self).__init__()
# define the TCN layers
def forward(self, x):
# define the forward pass for TCN
class LSTM(nn.Module):
def __init__(self):
super(LSTM, self).__init__()
# define the LSTM layers
def forward(self, x, h, c):
# define the forward pass for LSTM
class EncoderDecoder(nn.Module):
def __init__(self):
super(EncoderDecoder, self).__init__()
self.encoder = TCN()
self.decoder = LSTM()
self.fc_layer = nn.Linear(hidden_size, output_size)
def forward(self, x, h, c):
x = self.encoder(x)
output, (h, c) = self.decoder(x, h, c)
output = self.fc_layer(output)
return output, (h, c)
该神经网络有一个TCN作为encoder，有一个LSTM作为decoder。 其中，TCN用于提取时间序列数据的特征，LSTM用于将提取的特征转换为预测结果。 相比于其他传统的时间序列模型，这种模型结构具有更高的精度和对更长的序列长度有效。
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