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cs.LG2026
Contrastive Representation-Guided Genetic Minority Oversampling for Imbalanced Time-Series Classification
Wenbin Pei, Yunrong Hao, Zhen Liu +4
Real-world time-series classification tasks often exhibit class imbalance, which can be extremely severe in some applications. To avoid training biased classifiers on imbalanced da…
cs.LG2026
Learning Recursive Multi-Scale Representations for Irregular Multivariate Time Series Forecasting
Boyuan Li, Zhen Liu, Yicheng Luo +1
Irregular Multivariate Time Series (IMTS) are characterized by uneven intervals between consecutive timestamps, which carry sampling pattern information valuable and informative fo…
cs.LG2026
A Unified Shape-Aware Foundation Model for Time Series Classification
Zhen Liu, Yucheng Wang, Boyuan Li +4
Foundation models pre-trained on large-scale source datasets are reshaping the traditional training paradigm for time series classification. However, existing time series foundatio…