LSTM预测算法预测未来两天数据python代码
时间: 2023-12-08 14:04:07 浏览: 46
以下是使用LSTM模型进行未来两天数据预测的Python代码示例:
```python
import numpy as np
import pandas as pd
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
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(dataset)
# 将数据集分为训练集和测试集
train_size = int(len(dataset) * 0.67)
test_size = len(dataset) - train_size
train, test = dataset[0:train_size, :], dataset[train_size:len(dataset), :]
# 将时间序列数据转换为监督学习问题
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 = 1
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)
# 将输入数据重构为LSTM的输入格式 [样本数, 时间步, 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]))
# 创建和拟合LSTM模型
model = Sequential()
model.add(LSTM(units=4, input_shape=(1, look_back)))
model.add(Dense(units=1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=100, batch_size=1, verbose=2)
# 使用训练好的模型进行预测
trainPredict = model.predict(trainX)
testPredict = model.predict(testX)
# 反归一化
trainPredict = scaler.inverse_transform(trainPredict)
trainY = scaler.inverse_transform([trainY])
testPredict = scaler.inverse_transform(testPredict)
testY = scaler.inverse_transform([testY])
# 计算均方根误差
from sklearn.metrics import mean_squared_error
trainScore = np.sqrt(mean_squared_error(trainY[0], trainPredict[:, 0]))
print('Train Score: %.2f RMSE' % (trainScore))
testScore = np.sqrt(mean_squared_error(testY[0], testPredict[:, 0]))
print('Test Score: %.2f RMSE' % (testScore))
# 预测未来两天的数据
last_two_days = dataset[-2:]
last_two_days = np.reshape(last_two_days, (last_two_days.shape[0], 1, last_two_days.shape[1]))
futurePredict = model.predict(last_two_days)
futurePredict = scaler.inverse_transform(futurePredict)
print('Future Predictions: \n', futurePredict)
```
其中,`data.csv`是存储时间序列数据的CSV文件,示例代码中使用了LSTM模型进行训练和预测,预测未来两天的数据时需要将最近两天的数据重构为LSTM的输入格式,并使用训练好的模型进行预测。最后,将预测结果反归一化,即可得到未来两天的数据预测结果。