Continuity and Ordinality Matter: Constraining Time Series Tokens for Effective Time Series Analysis with Large Language Models
This paper proposes COM, a strategy that integrates geometric constraints into token initialization and training to preserve the inherent continuity and ordinality of time series tokens, consistently improving the performance of token-based time series LLMs on multiple benchmarks.
Article intelligence
Key points
- Token-based time series LLMs overlook continuity and ordinality, limiting performance.
- COM applies geometric constraints during initialization and training to preserve these properties.
- Experiments show consistent improvements across multiple benchmarks with strong generalizability.
- Code is publicly available for reproducibility.
Why it matters
This matters because token-based time series LLMs overlook continuity and ordinality, limiting performance.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.28866] Continuity and Ordinality Matter: Constraining Time Series Tokens for Effective Time Series Analysis with Large Language Models
[Submitted on 22 May 2026]
Title:Continuity and Ordinality Matter: Constraining Time Series Tokens for Effective Time Series Analysis with Large Language Models
View a PDF of the paper titled Continuity and Ordinality Matter: Constraining Time Series Tokens for Effective Time Series Analysis with Large Language Models, by Musheng Li and 3 other authors
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Abstract:Token-based time series large language models (TS-LLMs) have emerged as a promising direction for time series analysis and reasoning. However, prior studies largely overlook the inherent continuity and ordinality of time series tokens, which substantially limits model performance. In this paper, we argue that preserving these properties in time series token embeddings is crucial for the effectiveness of token-based TS-LLMs. To this end, we propose COM (Continuity and Ordinality Matter), a continuity- and ordinality-aware strategy that integrates geometric constraints into both the initialization and training stages. Empirical results on multiple time series analysis benchmarks demonstrate that COM consistently improves the performance of token-based TS-LLMs, achieving competitive results and strong generalizability. Code is available at this https URL .
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.28866 [cs.LG]
(or arXiv:2605.28866v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.28866
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Musheng Li [view email] [v1] Fri, 22 May 2026 06:13:57 UTC (2,875 KB)
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