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...

2023-03-24 04:01| 来源: 网络整理| 查看: 265

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

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