LSTM实现多变量多步预测代码

以下是使用LSTM实现多变量多步预测的Python代码示例：

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

# 准备数据
data = pd.read_csv('data.csv')
data = data.dropna()
dataset = data.values
train_ratio = 0.8
train_size = int(len(dataset) * train_ratio)
train_data = dataset[:train_size, :]
test_data = dataset[train_size:, :]

# 定义函数，将数据转化为多步预测问题
def create_dataset(dataset, look_back, look_forward):
    data_X, data_Y = [], []
    for i in range(len(dataset)-look_back-look_forward):
        a = dataset[i:(i+look_back), :]
        data_X.append(a)
        data_Y.append(dataset[(i+look_back):(i+look_back+look_forward), :])
    return np.array(data_X), np.array(data_Y)

look_back = 10
look_forward = 5
train_X, train_Y = create_dataset(train_data, look_back, look_forward)
test_X, test_Y = create_dataset(test_data, look_back, look_forward)

# 创建LSTM模型
model = Sequential()
model.add(LSTM(64, input_shape=(look_back, train_X.shape[2]), return_sequences=True))
model.add(LSTM(32))
model.add(Dense(look_forward))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(train_X, train_Y, epochs=100, batch_size=32, verbose=2)

# 进行预测
train_predict = model.predict(train_X)
test_predict = model.predict(test_X)

# 结果可视化
import matplotlib.pyplot as plt
plt.plot(train_Y[:, 0, 0], label='Actual')
plt.plot(train_predict[:, 0, 0], label='Predicted')
plt.legend()
plt.show()

plt.plot(test_Y[:, 0, 0], label='Actual')
plt.plot(test_predict[:, 0, 0], label='Predicted')
plt.legend()
plt.show()

注意，这里的数据集需要满足以下要求：

- 数据集为多变量时间序列数据；
- 数据集中不应该有缺失值；
- 数据集的每个特征需要进行归一化操作，以避免某些特征对模型的影响过大。