写出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
时间: 2023-03-07 15:08:07 浏览: 65
EEMD:以下代码演示了如何使用EEMD在csv文件中读取多输入单输出的时序数据,并进行训练和预测:# 导入需要的库
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from pyeemd import EEMD# 读取csv文件
data = pd.read_csv('csv文件.csv')# 获取多输入参数
x_data = data.iloc[:, :-1]# 获取单输出参数
y_data = data.iloc[:, -1]# 对多输入参数进行归一化处理
scaler = MinMaxScaler()
x_data = scaler.fit_transform(x_data)# 将多输入参数转换为时序数据
eemd = EEMD()
x_data = eemd.multivariate_eemd(x_data)# 训练模型
model.fit(x_data, y_data)# 进行预测
y_pred = model.predict(x_data)
相关问题
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 读取 CSV 数据的多输入单输出的时序训练及预测代码。这个代码假设输入的 CSV 数据中有多个特征和一个标签,以及数据按照时间顺序排列。
首先,需要安装 PyTorch、XGBoost、LightGBM 等库。代码如下:
```python
import torch
import numpy as np
import pandas as pd
import xgboost as xgb
import lightgbm as lgb
from torch.utils.data import Dataset, DataLoader
from sklearn.preprocessing import MinMaxScaler
from statsmodels.tsa.seasonal import STL
from PyEMD import EEMD
```
然后,需要定义一些超参数,例如 EEMD 分解的模式数、STL 分解的周期数、LSTM 的时间步数等。代码如下:
```python
# 超参数
eemd_mode = 8 # EEMD 模式数
stl_period = 24 # STL 周期数
lstm_seq_len = 48 # LSTM 时间步数
lstm_hidden_size = 64 # LSTM 隐藏层大小
lstm_num_layers = 2 # LSTM 层数
lstm_dropout = 0.2 # LSTM Dropout 概率
xgb_params = {'objective': 'reg:squarederror', 'eval_metric': 'rmse', 'eta': 0.01, 'max_depth': 6, 'subsample': 0.8, 'colsample_bytree': 0.8} # XGBoost 参数
lgb_params = {'objective': 'regression', 'metric': 'rmse', 'learning_rate': 0.01, 'max_depth': 6, 'subsample': 0.8, 'colsample_bytree': 0.8} # LightGBM 参数
```
接下来,需要定义一个 PyTorch 的数据集类,用于加载 CSV 数据并进行 EEMD-STL 处理。代码如下:
```python
class CustomDataset(Dataset):
def __init__(self, file_path):
self.df = pd.read_csv(file_path)
self.scaler = MinMaxScaler()
self.eemd = EEMD(n_imfs=eemd_mode)
self.stl = STL(self.df.iloc[:, -1], period=stl_period)
def __len__(self):
return len(self.df) - lstm_seq_len
def __getitem__(self, idx):
data = self.df.iloc[idx:idx+lstm_seq_len, :-1].values
label = self.df.iloc[idx+lstm_seq_len, -1]
data = self.scaler.fit_transform(data)
data = self.eemd.eemd(data.T).T
data = np.array([self.stl.decompose(d).trend for d in data])
data = torch.from_numpy(data).float()
label = torch.tensor(label).float()
return data, label
```
然后,需要定义一个 PyTorch 的模型类,用于将数据输入 LSTM,然后通过 XGBoost 和 LightGBM 进行进一步预测。代码如下:
```python
class CustomModel(torch.nn.Module):
def __init__(self):
super(CustomModel, self).__init__()
self.lstm = torch.nn.LSTM(input
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代码。这段代码实现了从CSV文件中读取多个时序输入的数据,然后使用EEMD和STL进行数据去噪和降维处理,然后使用XGBoost和LightGBM进行特征提取和预测,最后使用ConvLSTM模型进行时序预测。
```python
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
from lightgbm import LGBMRegressor
from pytorch_forecasting import TimeSeriesDataSet
from pytorch_forecasting.models import LSTM, DeepAR
from pytorch_forecasting.metrics import SMAPE, RMSE
from statsmodels.tsa.seasonal import seasonal_decompose
from PyEMD import EMD
# Define constants
BATCH_SIZE = 64
LEARNING_RATE = 0.001
EPOCHS = 100
HORIZON = 24 # Number of time steps to predict
LOOKBACK = 168 # Number of time steps to look back
NUM_EMD_COMPONENTS = 5 # Number of EMD components to keep
NUM_STL_COMPONENTS = 3 # Number of STL components to keep
# Read data from CSV file
data = pd.read_csv('data.csv')
num_features = data.shape[1] - 1 # Number of input features
# Split data into training and validation sets
train_data, val_data = train_test_split(data, test_size=0.2)
# Define EEMD and STL transformers
class EEMDTransformer:
def __init__(self, num_components):
self.num_components = num_components
self.emd = EMD()
def fit_transform(self, X):
X_transformed = np.zeros((X.shape[0], self.num_components, X.shape[1]))
for i in range(X.shape[0]):
imfs = self.emd(X[i])
imfs = imfs[:self.num_components]
for j in range(self.num_components):
X_transformed[i, j, :] = imfs[j]
return X_transformed
class STLTransformer:
def __init__(self, num_components):
self.num_components = num_components
def fit_transform(self, X):
X_transformed = np.zeros((X.shape[0], self.num_components, X.shape[1]))
for i in range(X.shape[0]):
result = seasonal_decompose(X[i], model='additive', period=24)
for j in range(self.num_components):
X_transformed[i, j, :] = result.seasonal[j*X.shape[1]:(j+1)*X.shape[1]]
return X_transformed
# Define PyTorch dataset and data loader for training and validation sets
class TimeSeriesDataset(Dataset):
def __init__(self, data, lookback, horizon, num_emd_components, num_stl_components):
self.data = data.values[:, 1:]
self.lookback = lookback
self.horizon = horizon
self.emd_transformer = EEMDTransformer(num_emd_components)
self.stl_transformer = STLTransformer(num_stl_components)
self.scaler = None
def __len__(self):
return