nbeats时间序列预测代码
时间: 2024-03-22 11:38:50 浏览: 18
以下是一个简单的nbeats时间序列预测模型的代码示例:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
class Block(nn.Module):
def __init__(self, input_size, output_size, num_hidden_layers, theta_dim):
super(Block, self).__init__()
self.input_size = input_size
self.output_size = output_size
self.num_hidden_layers = num_hidden_layers
self.theta_dim = theta_dim
self.fc1 = nn.Linear(input_size, output_size)
self.fc2 = nn.Linear(output_size, output_size)
self.fc3 = nn.Linear(output_size, output_size)
self.fc4 = nn.Linear(output_size, output_size)
self.theta_fc1 = nn.Linear(output_size, theta_dim)
self.theta_fc2 = nn.Linear(theta_dim, theta_dim)
self.theta_fc3 = nn.Linear(theta_dim, theta_dim)
self.theta_fc4 = nn.Linear(theta_dim, theta_dim)
def forward(self, x):
x = self.fc1(x)
for i in range(self.num_hidden_layers):
x = torch.relu(self.fc2(x))
x = torch.relu(self.fc3(x))
x = self.fc4(x)
theta = torch.relu(self.theta_fc1(x))
theta = torch.relu(self.theta_fc2(theta))
theta = torch.relu(self.theta_fc3(theta))
theta = self.theta_fc4(theta)
return theta
class NBEATS(nn.Module):
def __init__(self, input_size, output_size, num_blocks, num_layers, theta_dim):
super(NBEATS, self).__init__()
self.input_size = input_size
self.output_size = output_size
self.num_blocks = num_blocks
self.num_layers = num_layers
self.theta_dim = theta_dim
self.stacks = nn.ModuleList([Block(input_size, output_size, num_layers, theta_dim) for _ in range(num_blocks)])
self.fc1 = nn.Linear(input_size, output_size)
self.fc2 = nn.Linear(output_size, output_size)
def forward(self, x):
x = self.fc1(x)
for stack in self.stacks:
theta = stack(x)
x = x - theta
x = torch.relu(self.fc2(x))
return x
# 定义超参数
input_size = 1
output_size = 1
num_blocks = 2
num_layers = 4
theta_dim = 8
learning_rate = 0.001
num_epochs = 1000
# 构建模型
model = NBEATS(input_size, output_size, num_blocks, num_layers, theta_dim)
# 定义损失函数和优化器
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
# 训练模型
for epoch in range(num_epochs):
# 生成随机时间序列数据
x = np.random.rand(input_size, 100)
y = np.sin(x)
x = torch.from_numpy(x).float()
y = torch.from_numpy(y).float()
# 前向传播和计算损失
optimizer.zero_grad()
outputs = model(x)
loss = criterion(outputs, y)
# 反向传播和优化
loss.backward()
optimizer.step()
# 打印训练状态
if (epoch+1) % 100 == 0:
print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))
```
这个示例代码中,我们定义了一个简单的nbeats模型,包含两个堆叠的块,每个块包含4个隐藏层和8个theta维度。我们使用随机生成的时间序列数据进行训练,并使用均方误差作为损失函数和Adam优化器进行优化。