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

以下是一个基于 PyTorch、EEMD、STL、XGBoost、LightGBM 和 ConvLSTM 的多输入单输出时序预测的代码示例。这个代码示例假设你已经安装好了 PyTorch、EEMD、XGBoost 和 LightGBM。

import pandas as pd
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import xgboost as xgb
import lightgbm as lgb
from PyEMD import EEMD
from statsmodels.tsa.seasonal import STL
from torch.utils.data import Dataset, DataLoader

# 定义数据加载器类
class MyDataset(Dataset):
    def __init__(self, X, y):
        self.X = X
        self.y = y

    def __len__(self):
        return len(self.X)

    def __getitem__(self, idx):
        return self.X[idx], self.y[idx]

# 定义 EEMD 函数
def eemd_decomposition(data):
    eemd = EEMD()
    IMF = eemd(data)
    return IMF

# 定义 STL 函数
def stl_decomposition(data):
    stl = STL(data)
    res = stl.fit()
    seasonal = res.seasonal
    return seasonal

# 定义 LSTM 模型
class LSTM(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, output_size):
        super(LSTM, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
        out, _ = self.lstm(x, (h0, c0))
        out = self.fc(out[:, -1, :])
        return out

# 定义 XGBoost 模型
def xgb_train(X_train, y_train, X_test, y_test):
    xgb_model = xgb.XGBRegressor(objective='reg:squarederror', n_jobs=-1, max_depth=3)
    xgb_model.fit(X_train, y_train, eval_metric='rmse', eval_set=[(X_test, y_test)], early_stopping_rounds=10)
    return xgb_model

# 定义 LightGBM 模型
def lgb_train(X_train, y_train, X_test, y_test):
    lgb_model = lgb.LGBMRegressor(objective='regression', n_jobs=-1, max_depth=3)
    lgb_model.fit(X_train, y_train, eval_metric='rmse', eval_set=[(X_test, y_test)], early_stopping_rounds=10)
    return lgb_model

# 定义训练函数
def train(model, train_loader, criterion, optimizer):
    model.train()
    train_loss = 0
    for i, (inputs, targets) in enumerate(train_loader):
        inputs = inputs.to(device)
        targets = targets.to(device)
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, targets)
        train_loss += loss.item()
        loss.backward()
        optimizer.step()
    return train_loss / len(train_loader)

# 定义验证函数