LSTM实现股票预测pytorch

要使用LSTM实现股票预测，可以按照以下步骤进行： 1. 数据准备：收集股票历史数据，进行数据清洗和预处理，包括数据归一化、划分训练集和测试集等。 2. 模型搭建：使用PyTorch搭建LSTM模型，包括定义模型结构、设置超参数、选择损失函数和优化器等。 3. 模型训练：将训练集输入模型进行训练，得到模型参数。 4. 模型预测：将测试集输入模型进行预测，得到预测结果。 5. 结果评估：使用评价指标（如均方根误差RMSE）对预测结果进行评估。 以下是一个简单的LSTM股票预测代码示例： ```python import torch import torch.nn as nn import numpy as np import pandas as pd from sklearn.preprocessing import MinMaxScaler # 数据准备 data = pd.read_csv('stock_data.csv') data = data['Close'].values.reshape(-1, 1) scaler = MinMaxScaler() data = scaler.fit_transform(data) train_size = int(len(data) * 0.7) train_data = data[:train_size] test_data = data[train_size:] # 模型搭建 class LSTM(nn.Module): def __init__(self, input_size, hidden_size, output_size): super(LSTM, self).__init__() self.lstm = nn.LSTM(input_size, hidden_size, batch_first=True) self.fc = nn.Linear(hidden_size, output_size) def forward(self, x): out, _ = self.lstm(x) out = self.fc(out[:, -1, :]) return out input_size = 1 hidden_size = 32 output_size = 1 lr = 0.001 num_epochs = 100 model = LSTM(input_size, hidden_size, output_size) criterion = nn.MSELoss() optimizer = torch.optim.Adam(model.parameters(), lr=lr) # 模型训练 for epoch in range(num_epochs): inputs = torch.from_numpy(train_data[:-1]).float().unsqueeze(0) labels = torch.from_numpy(train_data[1:]).float().unsqueeze(0) outputs = model(inputs) loss = criterion(outputs, labels) optimizer.zero_grad() loss.backward() optimizer.step() if (epoch+1) % 10 == 0: print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item())) # 模型预测 model.eval() with torch.no_grad(): inputs = torch.from_numpy(test_data[:-1]).float().unsqueeze(0) labels = torch.from_numpy(test_data[1:]).float().unsqueeze(0) outputs = model(inputs) test_loss = criterion(outputs, labels) print('Test Loss: {:.4f}'.format(test_loss.item())) predicted = scaler.inverse_transform(outputs.numpy()) actual = scaler.inverse_transform(labels.numpy()) print('Predicted:', predicted.flatten()) print('Actual:', actual.flatten()) # 结果评估 from sklearn.metrics import mean_squared_error rmse = np.sqrt(mean_squared_error(actual, predicted)) print('RMSE:', rmse) ```