麻烦提供基于pytorch的EEMD+LightGBM+ConvLSTM的读取csv数据的多输入单输出的时序训练及预测代码

好的，下面是一个基于PyTorch的EEMD、LightGBM和ConvLSTM的时序训练和预测代码，用于多输入单输出的CSV数据。

首先，我们需要安装必要的Python库：

!pip install torch
!pip install lightgbm
!pip install sklearn
!pip install pandas

接下来，导入必要的库和函数：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader

import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler

import lightgbm as lgbm

import os
import csv
import time
from datetime import datetime

然后定义一些超参数：

# 超参数
num_epochs = 100
batch_size = 64
learning_rate = 0.001

num_eemd = 5 # EEMD分解次数
num_imf = 6 # 每个EEMD分解的IMF个数
num_conv = 64 # ConvLSTM中的卷积核数
hidden_size = 64 # ConvLSTM中的隐藏层维度
num_layers = 2 # ConvLSTM中的层数
dropout_rate = 0.2 # ConvLSTM中的dropout率
num_lgbm = 100 # LightGBM中的基学习器个数

接下来定义数据集和数据加载器：

class TimeSeriesDataset(Dataset):
    def __init__(self, data, input_window, output_window):
        self.data = data
        self.input_window = input_window
        self.output_window = output_window

    def __len__(self):
        return len(self.data) - self.input_window - self.output_window

    def __getitem__(self, idx):
        x = self.data[idx:idx+self.input_window]
        y = self.data[idx+self.input_window:idx+self.input_window+self.output_window]
        return x, y

def get_data_loader(data, input_window, output_window, batch_size):
    dataset = TimeSeriesDataset(data, input_window, output_window)
    data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True, drop_last=True)
    return data_loader

接下来定义EEMD模型：

class EEMD(nn.Module):
    def __init__(self, num_eemd, num_imf):
        super(EEMD, self).__init__()
        self.num_eemd = num_eemd
        self.num_imf = num_imf

        self.emd_layers = nn.ModuleList([nn.Sequential(
            nn.Conv1d(1, 64, kernel_size=3, padding=1),
            nn.BatchNorm1d(64),
            nn.ReLU(),
            nn.Conv1d(64, 64, kernel_size=3, padding=1),
            nn.BatchNorm1d(64),
            nn.ReLU(),
            nn.Conv1d(64, 64, kernel_size=3, padding=1),
            nn.BatchNorm1d(64),
            nn.ReLU(),
            nn.Conv1d(64, num_imf, kernel_size=1)
        ) for _ in range(num_eemd)])

    def forward(self, x):
        x = x.unsqueeze(1)
        imfs = []
        for i in range(self.num_eemd):
            res = x
            for j in range(self.num_imf-1):
                imf = self.emd