写一个lstm股票预测代码

好的，以下是一个简单的LSTM股票预测代码：

首先，我们需要准备一些必要的库和数据集：

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

# 读取股票数据集
df = pd.read_csv('stock_prices.csv')

# 选择我们要预测的股票价格列
data = df.filter(['Close'])

# 将数据集转换为numpy数组
dataset = data.values

# 将数据集划分为训练集和测试集
train_size = int(len(dataset) * 0.8)
train_data = dataset[0:train_size, :]
test_data = dataset[train_size:, :]

# 对数据进行缩放
scaler = MinMaxScaler(feature_range=(0, 1))
train_data = scaler.fit_transform(train_data)
test_data = scaler.transform(test_data)

接下来，我们需要定义一些辅助函数：

# 将数据集划分为X和Y
def create_dataset(dataset, time_step=1):
    X, Y = [], []
    for i in range(len(dataset) - time_step - 1):
        a = dataset[i:(i + time_step), 0]
        X.append(a)
        Y.append(dataset[i + time_step, 0])
    return np.array(X), np.array(Y)

# 定义时间步长
time_step = 100

# 创建训练集和测试集
X_train, Y_train = create_dataset(train_data, time_step)
X_test, Y_test = create_dataset(test_data, time_step)

# 将训练集和测试集重塑为LSTM的输入格式
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))

现在，我们可以定义我们的LSTM模型：

# 定义模型
model = Sequential()
model.add(LSTM(50, return_sequences=True, input_shape=(time_step, 1)))
model.add(LSTM(50, return_sequences=True))
model.add(LSTM(50))
model.add(Dense(1))

# 编译模型
model.compile(loss='mean_squared_error', optimizer='adam')

# 训练模型
model.fit(X_train, Y_train, validation_data=(X_test, Y_test), epochs=100, batch_size=64, verbose=1)

最后，我们使用训练好的模型进行预测：

# 使用模型进行预测
train_predict = model.predict(X_train)
test_predict = model.predict(X_test)

# 对预测结果进行反缩放
train_predict = scaler.inverse_transform(train_predict)
Y_train = scaler.inverse_transform([Y_train])
test_predict = scaler.inverse_transform(test_predict)
Y_test = scaler.inverse_transform([Y_test])

# 计算模型的RMSE
train_rmse = np.sqrt(np.mean((train_predict - Y_train) ** 2))
test_rmse = np.sqrt(np.mean((test_predict - Y_test) ** 2))
print('Train RMSE:', train_rmse)
print('Test RMSE:', test_rmse)

完整代码如下所示：

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

# 读取股票数据集
df = pd.read_csv('stock_prices.csv')

# 选择我们要预测的股票价格列
data = df.filter(['Close'])

# 将数据集转换为numpy数组
dataset = data.values

# 将数据集划分为训练集和测试集
train_size = int(len(dataset) * 0.8)
train_data = dataset[0:train_size, :]
test_data = dataset[train_size:, :]

# 对数据进行缩放
scaler = MinMaxScaler(feature_range=(0, 1))
train_data = scaler.fit_transform(train_data)
test_data = scaler.transform(test_data)

# 将数据集划分为X和Y
def create_dataset(dataset, time_step=1):
    X, Y = [], []
    for i in range(len(dataset) - time_step - 1):
        a = dataset[i:(i + time_step), 0]
        X.append(a)
        Y.append(dataset[i + time_step, 0])
    return np.array(X), np.array(Y)

# 定义时间步长
time_step = 100

# 创建训练集和测试集
X_train, Y_train = create_dataset(train_data, time_step)
X_test, Y_test = create_dataset(test_data, time_step)

# 将训练集和测试集重塑为LSTM的输入格式
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))

# 定义模型
model = Sequential()
model.add(LSTM(50, return_sequences=True, input_shape=(time_step, 1)))
model.add(LSTM(50, return_sequences=True))
model.add(LSTM(50))
model.add(Dense(1))

# 编译模型
model.compile(loss='mean_squared_error', optimizer='adam')

# 训练模型
model.fit(X_train, Y_train, validation_data=(X_test, Y_test), epochs=100, batch_size=64, verbose=1)

# 使用模型进行预测
train_predict = model.predict(X_train)
test_predict = model.predict(X_test)

# 对预测结果进行反缩放
train_predict = scaler.inverse_transform(train_predict)
Y_train = scaler.inverse_transform([Y_train])
test_predict = scaler.inverse_transform(test_predict)
Y_test = scaler.inverse_transform([Y_test])

# 计算模型的RMSE
train_rmse = np.sqrt(np.mean((train_predict - Y_train) ** 2))
test_rmse = np.sqrt(np.mean((test_predict - Y_test) ** 2))
print('Train RMSE:', train_rmse)
print('Test RMSE:', test_rmse)