You can train a single time-series model on unlabeled sensor data using text descriptions of what each sensor measures, and the resulting embeddings transfer well to multiple downstream tasks without task-specific retraining.
CHARM is a new model that learns meaningful representations from multivariate time-series data (like sensor readings) by combining channel descriptions with a Transformer architecture. It uses a self-supervised training approach called JEPA that predicts future embeddings rather than raw values, making it robust to sensor noise.