请帮我写一个关于LSTM预测的代码
时间: 2023-08-28 18:10:38 浏览: 38
好的,以下是一个使用LSTM进行时间序列预测的Python代码示例:
```python
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', usecols=[1], engine='python')
dataset = data.values
dataset = dataset.astype('float32')
# 数据归一化
scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(dataset)
# 划分训练集和测试集
train_size = int(len(dataset) * 0.67)
test_size = len(dataset) - train_size
train_data, test_data = dataset[0:train_size,:], dataset[train_size:len(dataset),:]
# 构建训练数据
def create_dataset(dataset, look_back=1):
dataX, dataY = [], []
for i in range(len(dataset)-look_back-1):
a = dataset[i:(i+look_back), 0]
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)
# 转换数据形状为 [样本数, 时间步长, 特征数]
trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1]))
# 构建LSTM模型
model = Sequential()
model.add(LSTM(4, input_shape=(1, look_back)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
# 训练模型
model.fit(trainX, trainY, epochs=100, batch_size=1, verbose=2)
# 测试模型
train_predict = model.predict(trainX)
test_predict = model.predict(testX)
# 反归一化
train_predict = scaler.inverse_transform(train_predict)
trainY = scaler.inverse_transform([trainY])
test_predict = scaler.inverse_transform(test_predict)
testY = scaler.inverse_transform([testY])
# 评估模型
train_score = np.sqrt(mean_squared_error(trainY[0], train_predict[:,0]))
test_score = np.sqrt(mean_squared_error(testY[0], test_predict[:,0]))
print('Train Score: %.2f RMSE' % (train_score))
print('Test Score: %.2f RMSE' % (test_score))
# 可视化结果
train_predict_plot = np.empty_like(dataset)
train_predict_plot[:, :] = np.nan
train_predict_plot[look_back:len(train_predict)+look_back, :] = train_predict
test_predict_plot = np.empty_like(dataset)
test_predict_plot[:, :] = np.nan
test_predict_plot[len(train_predict)+(look_back*2)+1:len(dataset)-1, :] = test_predict
plt.plot(scaler.inverse_transform(dataset), label='True Data')
plt.plot(train_predict_plot, label='Train Predict')
plt.plot(test_predict_plot, label='Test Predict')
plt.legend()
plt.show()
```
需要注意的是,这里的数据集是一个包含一列数据的csv文件,可以根据自己的需要修改数据读取和处理的部分。同时,该代码仅作为示例,实际使用中可能需要根据具体情况进行调整和优化。