lstm股票预测pytorch

LSTM (Long Short-Term Memory) 是一种递归神经网络 (RNN) 的变体，常用于处理序列数据的预测任务，包括股票价格预测。在Python中，可以使用各种深度学习框架（如TensorFlow、Keras、PyTorch等）来实现LSTM模型进行股票预测。

下面是一个使用Keras和TensorFlow实现LSTM模型进行股票预测的简单示例：

import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense

# 读取股票数据
data = pd.read_csv('stock_data.csv')

# 提取收盘价数据并进行归一化处理
close_prices = data['Close'].values.reshape(-1, 1)
scaler = MinMaxScaler()
normalized_prices = scaler.fit_transform(close_prices)

# 划分训练集和测试集
train_size = int(len(normalized_prices) * 0.8)
train_data, test_data = normalized_prices[:train_size], normalized_prices[train_size:]

# 准备训练数据
def create_dataset(data, lookback):
    X, y = [], []
    for i in range(len(data) - lookback - 1):
        X.append(data[i:(i + lookback), 0])
        y.append(data[i + lookback, 0])
    return np.array(X), np.array(y)

lookback = 10
X_train, y_train = create_dataset(train_data, lookback)
X_test, y_test = create_dataset(test_data, lookback)

# 调整输入数据的形状
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))

# 构建LSTM模型
model = Sequential()
model.add(LSTM(50, input_shape=