IMTS-Tokenizer: Time-Aware Tokenization for Irregular Multivariate Time Series Forecasting

Modeling Irregular Multivariate Time Series (IMTS) poses significant challenges due to asynchronous sampling and data sparsity. While existing methods focus on handling temporal irregularity after encoding, the tokenization stage itself remains underexplored. We propose IMTS-Tokenizer, a time aware tokenization framework built on the Irregular-time-aware Module (IRAM), which employs learnable temporal anchors and Gaussian-kernel attention to aggregate observations while preserving temporal information. To improve robustness under sparse and asynchronous sampling, the framework further incorporates Relative Time Feature (RTF) for pre-aggregation temporal encoding, Variable-Specific Anchor Initialization (VSA) for data-aligned anchor placement, anchor regularization for stable training, and multi-scale modeling. Experiments on four benchmarks demonstrate state-of-the-art (SOTA) performance, with the best results on seven out of eight metrics and statistically significant aggregate gains.