2026
Time-Frequency Augmented Multi-level Contrastive Clustering for Time Series
AAAI 2026technical
Current unsupervised time series clustering methods often struggle to fully exploit the inherent characteristics of time series data and commonly adopt a two-stage training strategy that separates feature learning from the clustering process. To address these limitations, this paper proposes a novel