Beyond Tokenization: Direct Timestep Embedding and Contrastive Alignment for Time-Series Question Answering
Recent advances in large language models (LLMs) have given rise to time-series question answering (TSQA), which formulates time-series analysis as natural-language question answering. However, directly feeding raw numerical series into LLMs suffers from a tokenization bottleneck: Byte Pair Encoding fragments continuous values into unstable tokens whose embeddings lack meaningful metric structure, resulting in the loss of magnitude, scale, and trend information. Prior methods use patch-based encoders that split the series into fixed windows, locking in one granularity that breaks patterns and hides exact timesteps, through a separate module that rarely transfers across datasets with differ...
| Authors | Yafeng Wu, Huu Hiep Nguyen, Thin Nguyen et al. |
|---|---|
| Primary category | cs.CL |
| Published | 17 June 2026 |
| Last updated at source | 17 June 2026 |
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