Giving Sensors a Voice: Multimodal JEPA for Semantic Time-Series Embeddings
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| Authors | Utsav Dutta et al. |
| Year | 2026 |
| Field | Machine Learning |
| arXiv | 2605.31580 |
| Download | |
| Categories | cs.LG |
Abstract
Transformer-based architectures have advanced sequence modeling in language and vision, yet general-purpose representation learning for heterogeneous multivariate time series remains underexplored. We introduce CHARM (Channel-Aware Representation Model), which incorporates channel-level textual descriptions into a Transformer encoder equivariant to channel order. CHARM is trained with a Joint Embedding Predictive Architecture (JEPA) and a novel loss promoting informative, temporally stable embeddings; latent-space prediction encourages robustness to sensor noise while description-aware gating provides interpretability through learned inter-channel relationships. Across anomaly detection, classification, and short- and long-term forecasting, the learned embeddings achieve strong performance using only a linear probe. Performance is driven primarily by the JEPA objective and conditioning architecture, with text descriptions serving as channel identifiers for cross-dataset generalization.
Engineering Breakdown
The Problem
Transformer-based architectures have advanced sequence modeling in language and vision, yet general-purpose representation learning for heterogeneous multivariate time series remains underexplored.
The Approach
We introduce CHARM (Channel-Aware Representation Model), which incorporates channel-level textual descriptions into a Transformer encoder equivariant to channel order.
Key Results
Across anomaly detection, classification, and short- and long-term forecasting, the learned embeddings achieve strong performance using only a linear probe.
Research Areas
This paper contributes to the following areas of AI/ML engineering:
- Model training
- Generalization
- Optimization
- Supervised learning
- Deep learning
- Multimodal
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