1D Convolutional Neural Network

A one-dimensional convolutional neural network (1D CNN) is a deep learning model that slides learned filters along one axis of ordered input, such as a signal or sequence, to detect local patterns for prediction.

1차원 합성곱 신경망(1D CNN)은 신호나 시퀀스처럼 순서가 있는 입력의 한 축을 따라 학습된 필터를 이동시키며 국소 패턴을 찾아 분류나 회귀를 수행하는 딥러닝 모형이다.

How do I classify signals or sequences using deep learning?

딥러닝으로 신호나 시퀀스 데이터를 어떻게 분류하나요?

A 1D convolutional neural network learns filters that detect local shapes in ordered data, such as peaks in a sensor signal or motifs in a sequence, then pools them for a prediction. It fits labelled sequential or spectral inputs of fixed length, such as ECG segments, accelerometer windows, or spectra.

When to use 1D Convolutional Neural Network

• Fixed-length signals such as ECG, audio, or accelerometer windows
• Spectra or other ordered feature vectors where neighboring values are related
• Classification or regression with many labelled examples

When to use something else

• Features are unordered tabular variables: use gradient boosting such as LightGBM
• Forecasting future values of a time series: use a temporal convolutional network or LSTM

Assumptions and requirements

• Sufficient labelled examples per class
• Inputs of fixed length (padded or windowed) and normalized
• Train, validation, and test splits without leakage between related segments
• Regularization such as dropout or early stopping to prevent overfitting

What the analysis reports

• Test-set accuracy, F1, AUC, or RMSE
• Training and validation loss curves
• Confusion matrix
• Architecture summary (filters, kernel sizes, layers)

How to report it

The 1D CNN achieved a test accuracy of [value] and macro F1 of [value] on [number] held-out segments.

Related methods

• Temporal Convolutional Network (TCN)
• LSTM Network
• Multilayer Perceptron

Run 1D Convolutional Neural Network on Quorant