摘要详情

ID / 提交时间

156 / 2024-08-31 20:41:32

标题

Contrastive Learning for Time Series Classification with Stronger Augmentations

关键字

Time series classification,data augmentation,contrastive learning

主题及专题

lTime Frequency Analysis

状态

全文录用

作者

WangZhouyang / 东南大学

MoLingfei / Southeast University

摘要

How to effectively represent time series data is critically important, as it enables the automatic extraction of features without extensive manual labeling. This capability is crucial in various fields, such as healthcare, where analyzing EEG signals can help diagnose sleep disorders or predict epileptic seizures. In this paper, we propose a novel contrastive learning framework for time series classification, which leverages both strong and weak augmentation strategies to enhance the learning process. We combine time domain and frequency domain augmentation techniques to form a strong augmentation strategy, allowing our model to capture a more comprehensive set of features from time series data. Using a convolutional neural network (CNN) as the backbone for the encoder, our model achieved an accuracy of 83.81% on the Sleep-EDF dataset and 97.73% on the Epilepsy dataset. These results demonstrate the effectiveness of combining strong and weak augmentations for contrastive learning in time series classification tasks.