有一个疫情数据时间序列，请帮我写一个LSTM模型用来预测后续天数的疫情数据

好的，我可以为您提供一个简单的LSTM模型，用于预测后续天数的疫情数据。以下是代码：

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

# 读取数据
data = pd.read_csv('data.csv')

# 数据预处理
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data = scaler.fit_transform(data['Confirmed'].values.reshape(-1, 1))

# 划分训练集和测试集
train_size = int(len(scaled_data) * 0.8)
test_size = len(scaled_data) - train_size
train_data = scaled_data[0:train_size, :]
test_data = scaled_data[train_size:len(scaled_data), :]

# 创建数据集
def create_dataset(dataset, time_step=1):
    dataX, dataY = [], []
    for i in range(len(dataset)-time_step-1):
        a = dataset[i:(i+time_step), 0]
        dataX.append(a)
        dataY.append(dataset[i + time_step, 0])
    return np.array(dataX), np.array(dataY)

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

# 转换为三维格式 [样本数, 时间步长, 特征数]
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))

# 创建LSTM模型
model = Sequential()
model.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], 1)))
model.add(LSTM(units=50))
model.add(Dense(units=1))
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=50, batch_size=64, verbose=1)

# 预测
train_predict = model.predict(X_train)
test_predict = model.predict(X_test)

# 反归一化
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])

# 评估模型
train_score = np.sqrt(np.mean(np.power(y_train - train_predict, 2)))
test_score = np.sqrt(np.mean(np.power(y_test - test_predict, 2)))
print('Train Score: %.2f RMSE' % (train_score))
print('Test Score: %.2f RMSE' % (test_score))

# 可视化结果
look_back = time_step
trainPredictPlot = np.empty_like(scaled_data)
trainPredictPlot[:, :] = np.nan
trainPredictPlot[look_back:len(train_predict)+look_back, :] = train_predict
testPredictPlot = np.empty_like(scaled_data)
testPredictPlot[:, :] = np.nan
testPredictPlot[len(train_predict)+(look_back*2)+1:len(scaled_data)-1, :] = test_predict
plt.plot(scaler.inverse_transform(scaled_data))
plt.plot(trainPredictPlot)
plt.plot(testPredictPlot)
plt.show()

请注意，这是一个简单的LSTM模型，仅用于演示如何使用LSTM来预测时间序列数据。您可以根据自己的需求进行调整和改进。