提供pytorch 的 多变量多步预测LSTM程序，从EXCEL文件加载数据，6个输入变量，1个输出变量（温度），要求预测未来12个时间步的温度,直接输出

好的，以下是一个基于PyTorch的多变量多步预测LSTM程序的示例代码，可以从Excel文件中加载数据，使用6个输入变量和1个输出变量（温度），并预测未来12个时间步的温度。请先确保已经安装了PyTorch和pandas库。

import torch
import torch.nn as nn
import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler

# 从Excel文件中加载数据
dataframe = pd.read_excel('data.xlsx')
dataset = dataframe.values
dataset = dataset.astype('float32')

# 归一化数据
scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(dataset)

# 划分数据集
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),:]

# 准备训练数据
def create_dataset(dataset, look_back, look_forward):
    dataX, dataY = [], []
    for i in range(len(dataset)-look_back-look_forward+1):
        a = dataset[i:(i+look_back), :]
        dataX.append(a)
        dataY.append(dataset[i+look_back:i+look_back+look_forward, 0])
    return np.array(dataX), np.array(dataY)

look_back = 12
look_forward = 12
trainX, trainY = create_dataset(train_data, look_back, look_forward)
testX, testY = create_dataset(test_data, look_back, look_forward)

# 将数据转换为PyTorch张量
trainX = torch.from_numpy(trainX)
trainY = torch.from_numpy(trainY)
testX = torch.from_numpy(testX)
testY = torch.from_numpy(testY)

# 定义LSTM模型
class LSTM(nn.Module):
    def __init__(self, input_size, hidden_size, output_size, num_layers):
        super(LSTM, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.lstm = nn.LSTM(input_size, hidden_size, num_layers)
        self.fc = nn.Linear(hidden_size, output_size)
        
    def forward(self, x):
        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).requires_grad_()
        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).requires_grad_()
        out, (hn, cn) = self.lstm(x, (h0.detach(), c0.detach()))
        out = self.fc(out[:, -1, :])
        return out
    
# 初始化模型和优化器
input_size = 6
hidden_size = 64
output_size = 1
num_layers = 2
lstm = LSTM(input_size, hidden_size, output_size, num_layers)
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(lstm.parameters(), lr=0.001)

# 训练模型
num_epochs = 100
for epoch in range(num_epochs):
    outputs = lstm(trainX)
    loss = criterion(outputs, trainY)
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    if epoch % 10 == 0:
        print("Epoch: %d, Loss: %f" % (epoch, loss.item()))

# 测试模型
lstm.eval()
train_predict = lstm(trainX)
test_predict = lstm(testX)

# 将预测结果转换为NumPy数组
train_predict = train_predict.data.numpy()
test_predict = test_predict.data.numpy()

# 反归一化预测结果
train_predict = scaler.inverse_transform(train_predict)
trainY = scaler.inverse_transform(trainY)
test_predict = scaler.inverse_transform(test_predict)
testY = scaler.inverse_transform(testY)

# 输出预测结果
print("训练集预测结果：")
print(train_predict[:,-look_forward:])
print("测试集预测结果：")
print(test_predict[:,-look_forward:])

在这个示例代码中，我们首先从Excel文件中加载数据，并使用MinMaxScaler将数据归一化到0到1之间。然后我们将数据集分为训练集和测试集，并使用create_dataset函数将数据转换为可以用于LSTM模型训练的格式。接下来我们定义了一个LSTM模型，并使用均方误差（MSE）作为损失函数，使用Adam优化器进行模型训练。最后我们使用训练好的模型对训练集和测试集进行预测，并将预测结果反归一化到原始数据范围内，输出预测结果。