CHARM

CHARM: Multimodal JEPA for Semantic Time-Series Embeddings via Channel-Aware Representation Learning

CHARM (Channel-Aware Representation Model) is a new Transformer-based architecture for general-purpose representation learning over heterogeneous multivariate time series. It integrates channel-level textual descriptions into a permutation-equivariant encoder trained with a Joint Embedding Predictive Architecture (JEPA) and a novel temporally stable embedding loss. The model achieves strong performance across anomaly detection, classification, and forecasting tasks using only a linear probe, with text descriptions primarily serving as channel identifiers enabling cross-dataset generalization.