写一个用jupyter实现LSTM预测的代码，用自己的含4种特征的1000*4的数据集，要实现训练集和测试集都有好的预测结果，要有评价指标，有对比图，要同LSTM自编码器预测的方法作比较

好的，我可以为您提供一个基础的LSTM预测代码，但是您需要自己将其修改为适合您的数据集和特征。以下是代码：

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

# 读取数据集
dataset = pd.read_csv('your_dataset.csv')

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

# 将数据集中的每个特征进行归一化处理
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data = scaler.fit_transform(data)

# 定义训练集和测试集
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), :]

# 创建训练集和测试集
def create_dataset(dataset, look_back=1):
    dataX, dataY = [], []
    for i in range(len(dataset) - look_back - 1):
        a = dataset[i:(i + look_back), :]
        dataX.append(a)
        dataY.append(dataset[i + look_back, 0])
    return np.array(dataX), np.array(dataY)

look_back = 10
trainX, trainY = create_dataset(train_data, look_back)
testX, testY = create_dataset(test_data, look_back)

# 将训练集和测试集重新调整为LSTM输入格式
trainX = np.reshape(trainX, (trainX.shape[0], look_back, trainX.shape[2]))
testX = np.reshape(testX, (testX.shape[0], look_back, testX.shape[2]))

# 定义LSTM模型
model = Sequential()
model.add(LSTM(50, input_shape=(look_back, train_data.shape[1]), return_sequences=True))
model.add(LSTM(50))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')

# 训练LSTM模型
model.fit(trainX, trainY, epochs=100, batch_size=64, verbose=2)

# 使用模型进行预测并将结果还原为原来的比例
train_predict = model.predict(trainX)
test_predict = model.predict(testX)
train_predict = scaler.inverse_transform(train_predict)
trainY = scaler.inverse_transform([trainY])
test_predict = scaler.inverse_transform(test_predict)
testY = scaler.inverse_transform([testY])

# 计算训练集和测试集的RMSE
train_rmse = np.sqrt(np.mean(np.power(trainY - train_predict, 2)))
test_rmse = np.sqrt(np.mean(np.power(testY - test_predict, 2)))
print('Train RMSE: %.2f' % (train_rmse))
print('Test RMSE: %.2f' % (test_rmse))

# 画出预测结果和真实结果的对比图
import matplotlib.pyplot as plt

train_predict_plot = np.empty_like(data)
train_predict_plot[:, :] = np.nan
train_predict_plot[look_back:len(train_predict)+look_back, :] = train_predict
test_predict_plot = np.empty_like(data)
test_predict_plot[:, :] = np.nan
test_predict_plot[len(train_predict)+(look_back*2)+1:len(data)-1, :] = test_predict
plt.plot(scaler.inverse_transform(data))
plt.plot(train_predict_plot)
plt.plot(test_predict_plot)
plt.show()

对于评价指标，我们采用RMSE（均方根误差）来评估模型的预测效果。同时，我们还可以将LSTM预测和LSTM自编码器预测的结果进行比较，并画出对比图。

以下是修改后的代码：

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

# 读取数据集
dataset = pd.read_csv('your_dataset.csv')

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

# 将数据集中的每个特征进行归一化处理
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data = scaler.fit_transform(data)

# 定义训练集和测试集
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), :]

# 创建训练集和测试集
def create_dataset(dataset, look_back=1):
    dataX, dataY = [], []
    for i in range(len(dataset) - look_back - 1):
        a = dataset[i:(i + look_back), :]
        dataX.append(a)
        dataY.append(dataset[i + look_back, 0])
    return np.array(dataX), np.array(dataY)

look_back = 10
trainX, trainY = create_dataset(train_data, look_back)
testX, testY = create_dataset(test_data, look_back)

# 将训练集和测试集重新调整为LSTM输入格式
trainX = np.reshape(trainX, (trainX.shape[0], look_back, trainX.shape[2]))
testX = np.reshape(testX, (testX.shape[0], look_back, testX.shape[2]))

# 定义LSTM模型
model = Sequential()
model.add(LSTM(50, input_shape=(look_back, train_data.shape[1]), return_sequences=True))
model.add(LSTM(50))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')

# 训练LSTM模型
model.fit(trainX, trainY, epochs=100, batch_size=64, verbose=2)

# 使用模型进行预测并将结果还原为原来的比例
train_predict = model.predict(trainX)
test_predict = model.predict(testX)
train_predict = scaler.inverse_transform(train_predict)
trainY = scaler.inverse_transform([trainY])
test_predict = scaler.inverse_transform(test_predict)
testY = scaler.inverse_transform([testY])

# 计算训练集和测试集的RMSE
train_rmse = np.sqrt(np.mean(np.power(trainY - train_predict, 2)))
test_rmse = np.sqrt(np.mean(np.power(testY - test_predict, 2)))
print('LSTM Train RMSE: %.2f' % (train_rmse))
print('LSTM Test RMSE: %.2f' % (test_rmse))

# 画出LSTM预测结果和真实结果的对比图
import matplotlib.pyplot as plt

train_predict_plot = np.empty_like(data)
train_predict_plot[:, :] = np.nan
train_predict_plot[look_back:len(train_predict)+look_back, :] = train_predict
test_predict_plot = np.empty_like(data)
test_predict_plot[:, :] = np.nan
test_predict_plot[len(train_predict)+(look_back*2)+1:len(data)-1, :] = test_predict
plt.plot(scaler.inverse_transform(data))
plt.plot(train_predict_plot)
plt.plot(test_predict_plot)
plt.title('LSTM Prediction vs Real Data')
plt.show()

# 定义LSTM自编码器模型
autoencoder = Sequential()
autoencoder.add(LSTM(50, input_shape=(look_back, train_data.shape[1]), return_sequences=True))
autoencoder.add(LSTM(50, return_sequences=True))
autoencoder.add(LSTM(50))
autoencoder.add(Dense(look_back))
autoencoder.compile(loss='mean_squared_error', optimizer='adam')

# 训练LSTM自编码器模型
autoencoder.fit(trainX, trainX, epochs=100, batch_size=64, verbose=2)

# 使用LSTM自编码器模型进行预测并将结果还原为原来的比例
train_predict_autoencoder = autoencoder.predict(trainX)
test_predict_autoencoder = autoencoder.predict(testX)
train_predict_autoencoder = scaler.inverse_transform(train_predict_autoencoder)
test_predict_autoencoder = scaler.inverse_transform(test_predict_autoencoder)

# 计算训练集和测试集的RMSE
train_rmse_autoencoder = np.sqrt(np.mean(np.power(train_data - train_predict_autoencoder, 2)))
test_rmse_autoencoder = np.sqrt(np.mean(np.power(test_data - test_predict_autoencoder, 2)))
print('LSTM Autoencoder Train RMSE: %.2f' % (train_rmse_autoencoder))
print('LSTM Autoencoder Test RMSE: %.2f' % (test_rmse_autoencoder))

# 画出LSTM预测和LSTM自编码器预测的结果对比图
train_predict_autoencoder_plot = np.empty_like(data)
train_predict_autoencoder_plot[:, :] = np.nan
train_predict_autoencoder_plot[look_back:len(train_predict_autoencoder)+look_back, :] = train_predict_autoencoder
test_predict_autoencoder_plot = np.empty_like(data)
test_predict_autoencoder_plot[:, :] = np.nan
test_predict_autoencoder_plot[len(train_predict_autoencoder)+(look_back*2)+1:len(data)-1, :] = test_predict_autoencoder
plt.plot(scaler.inverse_transform(data))
plt.plot(train_predict_plot)
plt.plot(train_predict_autoencoder_plot)
plt.title('LSTM vs LSTM Autoencoder (Train Set)')
plt.legend(['Real Data', 'LSTM Prediction', 'LSTM Autoencoder Prediction'], loc='upper left')
plt.show()
plt.plot(scaler.inverse_transform(data))
plt.plot(test_predict_plot)
plt.plot(test_predict_autoencoder_plot)
plt.title('LSTM vs LSTM Autoencoder (Test Set)')
plt.legend(['Real Data', 'LSTM Prediction', 'LSTM Autoencoder Prediction'], loc='upper left')
plt.show()

在上面的代码中，我们首先定义了一个LSTM模型，并将其用于预测。然后，我们定义了一个LSTM自编码器模型，并将其用于与LSTM模型进行比较。最后，我们计算了LSTM模型和LSTM自编码器模型的RMSE，并画出了预测结果的对比图。

希望这个代码能够帮助到您。如果您有任何问题，请随时联系我。