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Python中LSTM回归神经网络时间序列预测详情

以下是Python中LSTM回归神经网络时间序列预测的完整攻略,包括两个示例。

LSTM回归神经网络时间序列预测的基本步骤

LSTM回归神经网络时间序预测的基本步骤如下:

  1. 导入必要的库
import numpy as
import pandas as pd
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
  1. 准备数据

准备时间序列数据,将其转换为适合LSTM模的格式。

class TimeSeriesDataset(Dataset):
    def __init__(self, data, seq_len):
        self.data = data
        self.seq_len = seq_len

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

    def __getitem__(self, idx):
        x = self.data[idx:idx+self.seq_len]
        y = self.data[idx+self.seq_len]
        return x, y

# 加载数据
data = pd.read_csv('data.csv', header=None)
data = data.values.astype('float32')

# 划分训练集和测试集
train_size int(len(data) * 0.8)
train_data = data[:train_size]
test_data = data[train_size:]

# 标准化数据
mean = train_data.mean(axis=0)
std = train_data.std(axis=0)
train_data = (train_data - mean) / std
test_data = (test_data - mean) / std

# 创建数据集
seq_len = 10
train_dataset = TimeSeriesDataset(train_data, seq_len)
test_dataset = TimeSeriesDataset(test_data, seq_len)

# 创建数据加载器
batch_size = 32
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
  1. 定义模型

定义LSTM模型。

class LSTM(nn.Module):
    def __init__(self, input_size,_size, num_layers, output_size):
        super(LSTM, self).__init__()
        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):
        out, _ = self.lstm(x)
        out = out[:, -1, :]
        out = self.fc(out)
        return out
  1. 训练模型

训练LSTM模型。

# 定义模型
input_size = 1
hidden_size = 32
num_layers 2
output_size = 1
model = LSTM(input_size, hidden_size, num_layers, output_size)

# 定义损失函数和优化器
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)

# 训练模型
num_epochs = 100
for epoch in range(num_epochs):
    for i, (x, y) in enumerate(train_loader):
        # 前向传播
        x = x.unsqueeze(-1)
        y_pred = model(x)

        # 计算损失
        loss = criterion(y_pred, y)

        # 反向传播和优化
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

    # 打印损失
    print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))
  1. 测试模型

测试LSTM模型。

# 测试模型
model.eval()
with torch.no_grad():
    y_pred = []
    for x, y in test_loader:
        x = x.unsqueeze(-1)
        y_pred.append(model(x).squeeze().numpy())
    y_pred = np.concatenate(y_pred)

# 反标准化数据
y_pred = y_pred * std[-1] + mean[-1]
y_true = test_data[seq_len:, -1] * std[-1] + mean[-1]

# 绘制预测结果
plt.plot(y_true, label='True')
plt.plot(y_pred, label='Predicted')
plt.legend()
plt.show()

以上是Python中LSTM回归神经网络时间序列预测的完整攻略,通过以上步骤和示例,我们可以轻松地使用LSTM模型进行时间序列预测。

示例一:使用LSTM模型预测股票价格

以下是使用LSTM模型预测股票价格的示例代码:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader

# 加载数据
data = pd.read_csv('stock.csv')
data = data['Close'].values.astype('float32')

# 划分训练集和测试集
train_size = int(len(data) * 0.8)
train_data = data[:train_size]
test_data = data[train_size:]

# 标准化数据
mean = train_data.mean()
std = train_data.std()
train_data = (train_data - mean) / std
test_data = (test_data - mean) / std

# 创建数据集
seq_len = 10
train_dataset = TimeSeriesDataset(train_data, seq_len)
test_dataset = TimeSeriesDataset(test_data, seq_len)

# 创建数据加载器
batch_size = 32
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

# 定义模型
input_size = 1
hidden_size = 32
num_layers = 2
output_size = 1
model = LSTM(input_size, hidden_size, num_layers, output_size)

# 定义损失函数和优化器
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)

# 训练模型
num_epochs = 100
for epoch in range(num_epochs):
    for i, (x, y) in enumerate(train_loader):
        # 前向传播
        x = x.unsqueeze(-1)
        y_pred = model(x)

        # 计算损失
        loss = criterion(y_pred, y)

        # 反向传播和优化
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

    # 打印损失
    print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))

# 测试模型
model.eval()
with torch.no_grad():
    y_pred = []
    for x, y in test_loader:
        x = x.unsqueeze(-1)
        y_pred.append(model(x).squeeze().numpy())
    y_pred = np.concatenate(y_pred)

# 反标准化数据
y_pred = y_pred * std + mean
y_true = test_data[seq_len:] * std + mean

# 绘制预测结果
plt.plot(y, label='True')
plt.plot(y_pred, label='Predicted')
plt.legend()
plt.show()

上面的代码使用LSTM模型预测股票价格。

示例二:使用LSTM模型预测气温

以下使用LSTM模型预测气温的示例代码:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader

# 加载数据
data = pd.read_csv('temperature.csv')
data = data['Temperature'].values.astype('float32')

# 划分训练集和测试集
train_size = int(len(data) * 0.8)
train_data = data[:train_size]
test_data = data[train_size:]

# 标准化数据
mean = train_data.mean()
std = train_data.std()
train_data = (train_data - mean) / std
test_data = (test_data - mean) / std

# 创建数据集
seq_len = 10
train_dataset = TimeSeriesDataset(train_data, seq_len)
test_dataset = TimeSeriesDataset(test_data, seq_len)

# 创建数据加载器
batch_size = 32
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

# 定义模型
input_size = 1
hidden_size = 32
num_layers = 2
output_size = 1
model LSTM(input_size, hidden_size, num_layers, output_size)

# 定义损失函数和优化器
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)

# 训练模型
num_epochs = 
for epoch in range(num_epochs):
    for i, (x, y) in enumerate(train_loader):
        # 前向传播
        x = x.unsqueeze(-1)
        y_pred = model(x)

        # 计算损失
        loss = criterion(y_pred, y)

        # 反向传播和优化
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

    # 打印损失
    print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))

# 测试模型
model.eval()
with torch.no_grad():
    y_pred = []
    for x, y in test_loader:
        x = x.unsqueeze(-1)
        y_pred.append(model(x).squeeze().numpy())
    y_pred = np.concatenate(y_pred)

# 反标准化数据
y_pred = y_pred * std + mean
y_true = test_data[seq_len:] * std + mean

# 绘制预测结果
plt.plot(y_true, label='True')
plt.plot(y_pred label='Predicted')
plt.legend()
plt.show()

上面的代码使用LSTM模型预测气温。

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