Exploring the Potential of Probabilistic Transformer for Time Series Modeling: A Report on the ST-PT Framework
The Probabilistic Transformer (PT) establishes that the Transformer's self-attention plus its feed-forward block is mathematically equivalent to Mean-Field Variational Inference (MFVI) on a Conditional Random Field (CRF). Under this equivalence the Transformer ceases to be a black-box neural network and becomes a programmable factor graph: graph topology, factor potentials, and the message-passing schedule are all explicit and inspectable primitives that can be engineered. PT was originally developed for natural language and in this report we investigate its potential for time series. We first lift PT into the Spatial-Temporal Probabilistic Transformer (ST-PT) to repair PT's missing chann...
| Authors | Zhangzhi Xiong, Haoyi Wu, You Wu et al. |
|---|---|
| Primary category | cs.LG |
| Published | 29 April 2026 |
| Last updated at source | 29 April 2026 |
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