Pi-transformer: A prior-informed dual-attention model for multivariate time-series anomaly detection

ArXi:2509.19985v2 Announce Type: replace Anomalies in multivariate time series often arise from temporal context and cross-channel coordination rather than isolated outliers. We present Pi-Transformer (Prior-Informed Transformer), a transformer with two attention pathways: data-driven series attention and a smoothly evolving prior attention that encodes temporal invariants such as scale-related self-similarity and phase synchrony. The prior provides an amplitude-insensitive temporal reference that calibrates reconstruction error. During.