麻烦提供基于pytorch的EEMD+STL+XGBoost+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

以下是基于PyTorch的EEMD-STL-XGBoost-LightGBM-ConvLSTM的多输入单输出时序训练和预测的代码：

首先，需要安装以下库：
- torch：PyTorch深度学习库
- numpy：Python数值计算库
- pandas：Python数据分析库
- xgboost：XGBoost梯度提升库
- lightgbm：LightGBM梯度提升库
- pyeemd：EEMD分解库

import torch
import numpy as np
import pandas as pd
import xgboost as xgb
import lightgbm as lgb
from pyeemd import ceemdan
from statsmodels.tsa.seasonal import STL
from torch.utils.data import Dataset, DataLoader
from torch import nn
from typing import List

# 定义数据集类
class TimeSeriesDataset(Dataset):
    def __init__(self, data: pd.DataFrame, seq_length: int, target_col: str, 
                 features_cols: List[str], transform=None):
        self.seq_length = seq_length
        self.target_col = target_col
        self.features_cols = features_cols
        self.transform = transform
        
        self.data = data.sort_index().reset_index(drop=True)
        self.data = self.data[self.features_cols + [self.target_col]]
        self.data = self.data.dropna()
        
        self.X, self.y = self._prepare_data(self.data)
        
    def __len__(self):
        return len(self.X)
    
    def __getitem__(self, index):
        x = self.X[index]
        y = self.y[index]
        
        if self.transform:
            x = self.transform(x)
            
        return x, y
    
    def _prepare_data(self, data):
        X, y = [], []
        
        for i in range(len(data) - self.seq_length):
            X.append(data.iloc[i:i+self.seq_length][self.features_cols].values)
            y.append(data.iloc[i+self.seq_length][self.target_col])
            
        X = np.array(X)
        y = np.array(y)
        
        return X, y


# 定义 EEMD-STL 函数
def eemd_stl(df, feature_cols, target_col):
    # 对特征列进行 EEMD 分解
    X = df[feature_cols].values
    X_imfs = ceemdan(X)
    X_imfs = np.transpose(X_imfs, [1, 0, 2])

    # 对目标列进行 STL 分解
    stl = STL(df[target_col], period=365)
    res = stl.fit()
    y_seasonal, y_trend, y_residual = res.seasonal, res.trend, res.resid

    # 对 EEMD 分解后的结果进行 STL 分解
    X_imfs_stl = np.zeros_like(X_imfs)
    for i in range(X_imfs.shape[0]):
        imf_stl = STL(X_imfs[i], period=365).fit()
        X_imfs_stl[i] = imf_stl.trend

    # 合并 EEMD 分解后的结果和 STL 分解后的结果
    X_stl = np.concatenate([X_imfs_stl, np.expand_dims(y_seasonal, axis=1)], axis=1)
    y_stl = y_trend + y_residual
    
    return X_stl, y_stl


# 定义 ConvLSTM 模型
class