Kavli Affiliate: Xiang Zhang
| First 5 Authors: Ziyu Liu, Ziyu Liu, , ,
| Summary:
Self-supervised learning (SSL) for multivariate time series mainly includes
two paradigms: contrastive methods that excel at instance discrimination and
generative approaches that model data distributions. While effective
individually, their complementary potential remains unexplored. We propose a
Contrastive Generative Time series framework (CoGenT), the first framework to
unify these paradigms through joint contrastive-generative optimization. CoGenT
addresses fundamental limitations of both approaches: it overcomes contrastive
learning’s sensitivity to high intra-class similarity in temporal data while
reducing generative methods’ dependence on large datasets. We evaluate CoGenT
on six diverse time series datasets. The results show consistent improvements,
with up to 59.2% and 14.27% F1 gains over standalone SimCLR and MAE,
respectively. Our analysis reveals that the hybrid objective preserves
discriminative power while acquiring generative robustness. These findings
establish a foundation for hybrid SSL in temporal domains. We will release the
code shortly.
| Search Query: ArXiv Query: search_query=au:”Xiang Zhang”&id_list=&start=0&max_results=3