VarDim-Transformer:A Unified Framework for Variable-Dimension Time Series with Unknown Missing Patterns
简介：Conventional Transformer models for multivariate time series(MTS)rely on fixed-dimension inputs,necessitating explicit masking or pre-filling strategies when handling missing data.These strate-gies can distort observational distributions,underestimate predictive uncertainty,and introduce bias,particularly in scenarios characterized by dynamic dimensionality and unknown missing patterns.To address these challenges,the authors propose VarDim-Transformer,a novel architecture that na-tively supports variable input dimensions without requiring padding or channel identifiers,leverag-ing the Semi-Tensor Product(STP)of matrices.The core mechanism,the PiRegistry,dynamically projects arbitrary-length observation vectors into a unified latent feature space,enabling interaction via VarDim-Attention and VarDim-FFN before inverse projection.The authors evaluate the model under a rigorous"Random Dynamic Two-Level Missingness"protocol,which simulates long-term sen-sor failure and transient packet loss under privacy constraints.Experiments on the C-MAPSS FD001 remaining-useful-life prediction task demonstrate that VarDim-Transformer significantly outperforms imputation-based baselines.Notably,in a"Top-K"worst-case error analysis,VarDim-Transformer re-duces the penalized error score by 21.28％compared to baselines and achieves a 77.1％win rate on the most critical samples.This confirms its superior robustness and generalization capability in extreme,privacy-sensitive missingness scenarios.展开
关键词：Cheng projectionmissing dataPrivacy preservationRemaining useful lifesemi-tensor productvariable dimension transformer
在线出版日期：2026-05-14 （网站首发日期）
