Grounding Time-Series Foundation Models in Digital Twin Topology for Predictive Maintenance
eess.SP, cs.AI
Submitted: 2026-09-30
Updated: 2026-09-30
Terminology
Sources
- Chronos-2: From Univariate to Universal Forecasting
- Chronos: Learning the Language of Time Series
- AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- MOMENT: A Family of Open Time-series Foundation Models
- A No-Free-Lunch Theorem for MultiTask Learning
- The Digital Twin Landscape at the Crossroads of Predictive Maintenance, Machine Learning and Physics Based Modeling
- State-of-the-Art Review: The Use of Digital Twins to Support Artificial Intelligence-Guided Predictive Maintenance
- Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization
- A Survey of Deep Learning and Foundation Models for Time Series Forecasting
- Learning Transferable Visual Models From Natural Language Supervision
- Position: Foundation Models Need Digital Twin Representations
- Hierarchical Graph Representation Learning with Differentiable Pooling
- A Survey of Graph Transformers: Architectures, Theories and Applications
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