pytorch实现CNN和Bi-Transformer时间序列预测
时间: 2023-10-14 08:21:23 浏览: 76
对于时间序列预测问题,CNN和Bi-Transformer是两种常用的模型。下面是使用PyTorch实现这两种模型的代码示例:
1. 使用CNN进行时间序列预测
```python
import torch
import torch.nn as nn
class CNN(nn.Module):
def __init__(self, input_size, hidden_size, kernel_size):
super(CNN, self).__init__()
self.conv = nn.Conv1d(in_channels=input_size, out_channels=hidden_size, kernel_size=kernel_size)
self.relu = nn.ReLU()
self.pool = nn.MaxPool1d(kernel_size=2)
def forward(self, x):
x = self.conv(x)
x = self.relu(x)
x = self.pool(x)
return x
class TimeSeriesCNN(nn.Module):
def __init__(self, input_size, hidden_size, kernel_size):
super(TimeSeriesCNN, self).__init__()
self.cnn1 = CNN(input_size, hidden_size, kernel_size)
self.cnn2 = CNN(hidden_size, hidden_size, kernel_size)
self.linear = nn.Linear(hidden_size, 1)
def forward(self, x):
x = self.cnn1(x)
x = self.cnn2(x)
x = x.flatten(start_dim=1)
x = self.linear(x)
return x
```
2. 使用Bi-Transformer进行时间序列预测
```python
import torch
import torch.nn as nn
class BiTransformer(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, num_heads, dropout):
super(BiTransformer, self).__init__()
self.encoder = nn.TransformerEncoder(nn.TransformerEncoderLayer(
d_model=input_size, nhead=num_heads, dim_feedforward=hidden_size, dropout=dropout), num_layers=num_layers)
self.decoder = nn.TransformerDecoder(nn.TransformerDecoderLayer(
d_model=input_size, nhead=num_heads, dim_feedforward=hidden_size, dropout=dropout), num_layers=num_layers)
self.linear = nn.Linear(input_size, 1)
def forward(self, x):
x = self.encoder(x)
x = self.decoder(x)
x = self.linear(x)
return x
```
以上是使用PyTorch实现CNN和Bi-Transformer进行时间序列预测的代码示例,可以根据具体问题进行修改和调整。
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