CVTt
CVTt, or Continuous Vector Time-transformer, is a conceptual framework in time-series processing that applies transformer-style attention to continuous-time data represented as vector tokens. It is designed to handle irregular sampling, non-uniform time intervals, and long-range temporal dependencies more flexibly than fixed-window approaches.
Core ideas center on representing observations as time-aware vector tokens and using attention mechanisms that incorporate
Relation to existing work reflects an effort to blend strengths of Transformer architectures with continuous-time modeling.
Applications and evaluation focus on domains with irregular or high-frequency data, including sensor networks, healthcare monitoring,
Limitations include computational cost for long or dense sequences, sensitivity to time-encoding choices, and the need