1d Cnn Time Series Classification Pytorch

6 min read Jul 07, 2024
1d Cnn Time Series Classification Pytorch

1D CNN for Time Series Classification using PyTorch


Time series classification is a common task in many fields, including finance, healthcare, and climate science. With the rise of deep learning, convolutional neural networks (CNNs) have been widely adopted for time series classification tasks. In this article, we'll explore how to implement a 1D CNN for time series classification using PyTorch.

What is 1D CNN?

A 1D CNN is a type of neural network that is specifically designed for sequence data, such as time series data. Unlike traditional CNNs that operate on 2D images, 1D CNNs operate on 1D sequences. The architecture of a 1D CNN is similar to that of a traditional CNN, but with some key differences:

  • Convolutional layers: Instead of using 2D convolutional layers, 1D CNNs use 1D convolutional layers that scan the input sequence in a single direction (e.g., from left to right).
  • Max pooling layers: 1D CNNs use max pooling layers that downsample the input sequence by taking the maximum value across a fixed window size.

Time Series Classification using 1D CNN

Time series classification involves assigning a label to a sequence of values based on its patterns and characteristics. For example, in finance, we might want to classify stock prices as "up" or "down" based on their historical trends.

Here's an overview of the time series classification process using 1D CNN:

Step 1: Data Preparation

  • Collect and preprocess the time series data, including normalization and feature extraction.
  • Split the data into training and testing sets.

Step 2: Model Definition

  • Define a 1D CNN model using PyTorch, including:
    • Convolutional layers with 1D kernels.
    • Max pooling layers with a fixed window size.
    • Flatten layer to reshape the output of the convolutional layers.
    • Dense layer(s) for classification.

Step 3: Model Training

  • Train the 1D CNN model using the training data, with a suitable loss function (e.g., cross-entropy loss) and optimizer (e.g., Adam).

Step 4: Model Evaluation

  • Evaluate the performance of the trained model on the testing data, using metrics such as accuracy, precision, and recall.

PyTorch Implementation

Here's an example PyTorch implementation of a 1D CNN for time series classification:

import torch
import torch.nn as nn
import torch.optim as optim

class OneDCNN(nn.Module):
    def __init__(self):
        super(OneDCNN, self).__init__()
        self.conv1 = nn.Conv1d(1, 10, kernel_size=5)
        self.pool = nn.MaxPool1d(2, 2)
        self.conv2 = nn.Conv1d(10, 20, kernel_size=5)
        self.fc = nn.Linear(20 * 10, 2)  # output layer

    def forward(self, x):
        x = torch.relu(self.conv1(x))
        x = self.pool(x)
        x = torch.relu(self.conv2(x))
        x = x.view(-1, 20 * 10)
        x = self.fc(x)
        return x

model = OneDCNN()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

for epoch in range(10):
    for x, y in train_loader:
        # Forward pass
        outputs = model(x)
        loss = criterion(outputs, y)

        # Backward and optimize
    print(f'Epoch {epoch+1}, Loss: {loss.item()}')

# Evaluate the model on the test data
test_loader = DataLoader(test_data, batch_size=32, shuffle=False)
test_loss = 0
correct = 0
with torch.no_grad():
    for x, y in test_loader:
        outputs = model(x)
        loss = criterion(outputs, y)
        test_loss += loss.item()
        _, predicted = torch.max(outputs, 1)
        correct += (predicted == y).sum().item()

accuracy = correct / len(test_data)
print(f'Test Loss: {test_loss / len(test_loader)}')
print(f'Test Accuracy: {accuracy:.2f}%')


In this article, we've explored the concept of 1D CNNs for time series classification using PyTorch. We've covered the architecture of a 1D CNN, the process of time series classification, and a PyTorch implementation of a 1D CNN model. By applying 1D CNNs to time series classification tasks, we can leverage the power of deep learning to identify complex patterns and relationships in sequence data.

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