Problem
Research questions and friction points this paper is trying to address.
irregular multivariate time series
time-aware
forecasting
A Hybrid Attention Model Learning Unified Time-aware Patch Representation for Irregular Multivariate Time Series Forecasting
๐ค AI Summary
่ฎบๆๆๅบไบไธ็งๆททๅๆณจๆๅๆจกๅ๏ผ้่ฟๆถ้ดๆ็ฅ็่กฅไธ็ผ็ ๅๆถ้ดๅ็ฝฎๆณจๆๅๆบๅถ่งฃๅณไธ่งๅๅคๅ้ๆถ้ดๅบๅ้ขๆต้ฎ้ขใ
๐ Abstract
Time series foundation models (TSFMs) have recently delivered impressive zero-shot performance across diverse forecasting tasks. However, real-world decision-making frequently relies on \emph{irregular multivariate time series} (IMTS), where inconsistent inter-observation intervals and asynchronous sampling across variables coexist with informative missingness. Existing TSFMs handle such inputs either through imputation that injects spurious values or through index-based positional encodings that ignore continuous time. There is still a gap in the foundation model that follows the original IMTS patterns. In this paper, we propose a hybrid attention model that learns a unified time-aware patch representation for IMTS forecasting. We first design a \emph{time-aware patch encoding} that maps a variable number of intra-patch timestamps into a fixed-size embedding, producing a uniform format for irregular patches without resorting to imputation. We then introduce a \emph{time bias attention} mechanism that calibrates inter-patch temporal misalignment and asynchronous cross-channel dependencies as auxiliary attention offset. Finally, on top of a decoder-only Transformer backbone, we adopt a \emph{hybrid causal mask} that preserves a bidirectional full view over the historical context while keeping the forecast horizon strictly autoregressive. To support large-scale pretraining under irregular settings, we also curate VersaTSA, an archive of $30$B observations that retains the native sampling sparsity of its sources. Experiments on three IMTS benchmarks and a standard regular-MTS benchmark show that our model achieves state-of-the-art zero-shot performance on IMTS and remains competitive when transferred to regular forecasting.
Innovation
Methods, ideas, or system contributions that make the work stand out.
time-aware patch encoding
time bias attention
hybrid causal mask
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