lstm 预测预报算法的 python 实现 - lstm 预测预报算法的 python 实现示例

以下是一个使用Python实现LSTM预测算法的示例代码：

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

# 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)

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

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

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

# 将输入数据重塑为LSTM所需的格式 [样本数，时间步长，特征数]
trainX = np.reshape(trainX, (trainX.shape[0], trainX.shape[1], 1))
testX = np.reshape(testX, (testX.shape[0], testX.shape[1], 1))

# 构建LSTM模型
model = Sequential()
model.add(LSTM(4, input_shape=(lookback, 1)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=100, batch_size=1, verbose=2)

# 预测
trainPredict = model.predict(trainX)
testPredict = model.predict(testX)

# 反向缩放预测结果
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
trainScore = np.sqrt(np.mean((trainY[0] - trainPredict[:, 0]) ** 2))
testScore = np.sqrt(np.mean((testY[0] - testPredict[:, 0]) ** 2))

print('Train Score: %.2f RMSE' % trainScore)
print('Test Score: %.2f RMSE' % testScore)

请注意，上述代码中的数据文件应为一个包含一列数值的CSV文件。