pytorch informer

Informer是一种用于时间序列预测的模型，它可以处理多变量和多步预测问题。下面是使用PyTorch实现Informer模型的步骤： 1. 安装依赖库 ```python !pip install torch !pip install tqdm !pip install numpy !pip install pandas !pip install matplotlib !pip install sklearn ``` 2. 导入必要的库 ```python import torch import torch.nn as nn import torch.nn.functional as F import numpy as np import pandas as pd from tqdm import tqdm import matplotlib.pyplot as plt from sklearn.preprocessing import StandardScaler ``` 3. 定义Informer模型 ```python class Encoder(nn.Module): def __init__(self, input_size, hidden_size, num_heads, seq_len): super(Encoder, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.num_heads = num_heads self.seq_len = seq_len self.multihead_attn = nn.MultiheadAttention(hidden_size, num_heads) self.layer_norm1 = nn.LayerNorm(hidden_size) self.pos_ffn = nn.Linear(hidden_size, hidden_size * 4) self.layer_norm2 = nn.LayerNorm(hidden_size) def forward(self, x): residual = x x = x.permute(1, 0, 2) attn_output, _ = self.multihead_attn(x, x, x) x = self.layer_norm1(residual + attn_output.permute(1, 0, 2)) residual = x x = self.pos_ffn(x) x = F.gelu(x) x = self.pos_ffn(x) x = self.layer_norm2(residual + x) return x class Decoder(nn.Module): def __init__(self, input_size, hidden_size, num_heads, seq_len): super(Decoder, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.num_heads = num_heads self.seq_len = seq_len self.masked_multihead_attn = nn.MultiheadAttention(hidden_size, num_heads) self.layer_norm1 = nn.LayerNorm(hidden_size) self.multihead_attn = nn.MultiheadAttention(hidden_size, num_heads) self.layer_norm2 = nn.LayerNorm(hidden_size) self.pos_ffn = nn.Linear(hidden_size, hidden_size * 4) self.layer_norm3 = nn.LayerNorm(hidden_size) def forward(self, x, encoder_output): residual = x x = x.permute(1, 0, 2) attn_output, _ = self.masked_multihead_attn(x, x, x, attn_mask=self._get_mask(x)) x = self.layer_norm1(residual + attn_output.permute(1, 0, 2)) residual = x x = self.multihead_attn(x, encoder_output, encoder_output) x = self.layer_norm2(residual + x) residual = x x = self.pos_ffn(x) x = F.gelu(x) x = self.pos_ffn(x) x = self.layer_norm3(residual + x) return x def _get_mask(self, x): mask = torch.ones(self.seq_len, self.seq_len).to(x.device) mask = torch.tril(mask) return mask class Informer(nn.Module): def __init__(self, input_size, output_size, hidden_size, num_encoder_layers, num_decoder_layers, num_heads, seq_len): super(Informer, self).__init__() self.input_size = input_size self.output_size = output_size self.hidden_size = hidden_size self.num_encoder_layers = num_encoder_layers self.num_decoder_layers = num_decoder_layers self.num_heads = num_heads self.seq_len = seq_len self.encoder_layers = nn.ModuleList() self.decoder_layers = nn.ModuleList() for i in range(num_encoder_layers): self.encoder_layers.append(Encoder(input_size, hidden_size, num_heads, seq_len)) for i in range(num_decoder_layers): self.decoder_layers.append(Decoder(input_size, hidden_size, num_heads, seq_len)) self.linear = nn.Linear(hidden_size, output_size) def forward(self, x): encoder_output = x for encoder_layer in self.encoder_layers: encoder_output = encoder_layer(encoder_output) decoder_output = x[:, -1:, :] for decoder_layer in self.decoder_layers: decoder_output = decoder_layer(decoder_output, encoder_output) output = self.linear(decoder_output[:, -1:, :]) return output ``` 4. 定义数据预处理函数 ```python def prepare_data(data, seq_len, train_ratio): data = data.values scaler = StandardScaler() data = scaler.fit_transform(data) data_x = [] data_y = [] for i in range(len(data) - seq_len): data_x.append(data[i:i+seq_len]) data_y.append(data[i+seq_len]) data_x = np.array(data_x) data_y = np.array(data_y) train_size = int(len(data_x) * train_ratio) train_x = data_x[:train_size, :, :] train_y = data_y[:train_size, :] test_x = data_x[train_size:, :, :] test_y = data_y[train_size:, :] return train_x, train_y, test_x, test_y, scaler ``` 5. 定义训练函数 ```python def train(model, train_x, train_y, test_x, test_y, epochs, lr): optimizer = torch.optim.Adam(model.parameters(), lr=lr) loss_func = nn.MSELoss() train_loss_list = [] test_loss_list = [] for epoch in tqdm(range(epochs)): model.train() train_loss = 0 for i in range(train_x.shape[0]): optimizer.zero_grad() x = torch.Tensor(train_x[i]).unsqueeze(0) y = torch.Tensor(train_y[i]).unsqueeze(0) output = model(x) loss = loss_func(output, y) loss.backward() optimizer.step() train_loss += loss.item() train_loss /= train_x.shape[0] train_loss_list.append(train_loss) model.eval() test_loss = 0 with torch.no_grad(): for i in range(test_x.shape[0]): x = torch.Tensor(test_x[i]).unsqueeze(0) y = torch.Tensor(test_y[i]).unsqueeze(0) output = model(x) loss = loss_func(output, y) test_loss += loss.item() test_loss /= test_x.shape[0] test_loss_list.append(test_loss) print('Epoch [{}/{}], train loss: {:.4f}, test loss: {:.4f}'.format(epoch+1, epochs, train_loss, test_loss)) return train_loss_list, test_loss_list ``` 6. 载入数据集并调用训练函数 ```python data = pd.read_csv('data.csv', index_col=0) train_x, train_y, test_x, test_y, scaler = prepare_data(data, seq_len=96, train_ratio=0.8) model = Informer(input_size=train_x.shape[-1], output_size=train_y.shape[-1], hidden_size=256, num_encoder_layers=2, num_decoder_layers=2, num_heads=8, seq_len=96) train_loss_list, test_loss_list = train(model, train_x, train_y, test_x, test_y, epochs=50, lr=0.001) ``` 7. 绘制损失曲线 ```python plt.plot(train_loss_list, label='train loss') plt.plot(test_loss_list, label='test loss') plt.legend() plt.show() ``` 这样就可以使用PyTorch实现Informer模型了。

好的，我可以为您提供一些关于用PyTorch实现Informer预测股票的步骤和建议。 Informer是一种基于Transformer的序列预测模型，可以用于预测时间序列数据，包括股票价格。下面是实现Informer预测股票的几个步骤： 1. 准备数据：首先需要收集股票价格数据，可以使用Yahoo Finance或Alpha Vantage等API获取历史价格数据。然后，将数据转换为可以用于训练模型的格式，例如时间序列数据集。 2. 构建模型：使用PyTorch实现Informer模型，可以参考Informer的论文和源代码。可以使用PyTorch提供的Transformer模块，或者实现自己的Transformer模块。 3. 训练模型：使用收集到的数据集，训练Informer模型。可以使用PyTorch提供的优化器和损失函数，例如Adam优化器和均方误差损失函数。 4. 预测股票价格：使用训练好的Informer模型，对未来的股票价格进行预测。可以使用PyTorch提供的预测函数，或者自己实现预测函数。 一些建议： - 在准备数据时，可以考虑使用滑动窗口技术，将时间序列数据转换为多个子序列，以便训练模型。还可以使用归一化技术，将数据缩放到一定的范围内，以便模型更好地学习数据特征。 - 在构建模型时，可以使用不同的超参数和模型结构进行实验，以找到最佳的模型。 - 在训练模型时，可以使用交叉验证技术，将数据集分为训练集和验证集，以便评估模型的性能。 - 在预测股票价格时，可以使用滚动预测技术，逐步预测未来的价格。还可以使用模型融合技术，结合多个Informer模型的预测结果，以提高预测准确率。 希望这些步骤和建议能对您有所帮助，祝您实现成功！