Foundation Models for Time Series: Extending Transformers Beyond Language
RISE researchers are exploring how transformer-based foundation models can generalize across domains with minimal retraining, tackling challenges in sensor data, healthcare, and climate systems.
While transformer models revolutionized natural language processing, applying them to time series data, from sensors, healthcare, and climate systems, presents new challenges. RISE researchers are exploring how foundation models can generalize across domains with minimal retraining.
Promising Models
Models like Informer (sparse attention for long sequences), TS2Vec (contrastive learning for unlabeled data), and Moment (masked modeling for multi-domain adaptation) show promise for classification, anomaly detection, and forecasting.
Challenges
However, time series data often features non-stationary dynamics, irregular sampling, and noise, challenges that require specialized architectures and pretraining strategies. RISE work focuses on improving these models’ robustness, efficiency, and interpretability for real-world applications.
Potential Impact
The potential payoff is significant: foundation models that can adapt across industries could make advanced AI analysis accessible even to organizations with limited data or technical capacity.
