提供基于pytorch的EEMD+STL+XGBoost+LightGBM+ConvLSTM的读取csv数据，前80%为训练集，后20%为测试集的多输入单输出的时序训练及预测代码

好的，以下是基于PyTorch的EEMD、STL、XGBoost、LightGBM和ConvLSTM的读取CSV数据、前80%为训练集，后20%为测试集的多输入单输出的时序训练及预测代码。代码中使用了PyTorch、NumPy、Pandas、scikit-learn、xgboost、lightgbm和PyTorch-Lightning等库。需要先安装这些库才能运行以下代码。

import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
import xgboost as xgb
import lightgbm as lgb
import torch
from torch import nn
from torch.utils.data import Dataset, DataLoader
import pytorch_lightning as pl

# 定义数据集类
class TimeSeriesDataset(Dataset):
    def __init__(self, data, lookback, target_col):
        self.lookback = lookback
        self.target_col = target_col
        self.data = data
        self.scaler = MinMaxScaler(feature_range=(0, 1))
        self.scaler.fit(self.data)

    def __len__(self):
        return len(self.data) - self.lookback

    def __getitem__(self, idx):
        idx += self.lookback
        x = self.data[idx - self.lookback:idx]
        x = self.scaler.transform(x)
        y = self.data[idx][self.target_col]
        return x, y

# 定义 EEMD 和 STL 模块
class EEMD(nn.Module):
    def __init__(self, n_imfs):
        super().__init__()
        self.n_imfs = n_imfs

    def forward(self, x):
        import eemd
        decomposer = eemd.ceemdan(x)
        imfs, _ = decomposer.decompose()
        return imfs[:self.n_imfs]

class STL(nn.Module):
    def __init__(self, period, seasonal):
        super().__init__()
        self.period = period
        self.seasonal = seasonal

    def forward(self, x):
        from stldecompose import decompose
        result = decompose(x, period=self.period, seasonal=self.seasonal)
        return result.seasonal

# 定义 ConvLSTM 模块
class ConvLSTM(nn.Module):
    def __init__(self, input_dim, hidden_dim, kernel_size, num_layers, dropout):
        super().__init__()
        self.input_dim = input_dim
        self.hidden_dim = hidden_dim
        self.kernel_size = kernel_size
        self.num_layers = num_layers
        self.dropout = dropout
        self.conv_lstm = nn.LSTM(input_size=input_dim,
                                 hidden_size=hidden_dim,
                                 kernel_size=kernel_size,
                                 num_layers=num_layers,
                                 dropout=dropout)

    def forward(self, x):
        output, _ = self.conv_lstm(x)
        return output[:, -1, :, :, :]

# 定义 PyTorch Lightning 模型
class TimeSeriesModel(pl.LightningModule):
    def __init__(self, input_dim, hidden_dim, kernel_size, num_layers, dropout, n_imfs, period, seasonal):
        super().__init__()
        self.eemd = EEMD(n_imfs=n_imfs)
        self.stl = STL(period=period, seasonal=seasonal)
        self.conv_lstm = ConvLSTM(input_dim=input_dim + n_imfs + 1,
                                  hidden_dim=hidden_dim,
                                  kernel_size=kernel_size,