Continuity and Ordinality Matter: Constraining Time Series Tokens for Effective Time Series Analysis with Large Language Models
arXiv:2605.28866v1 Announce Type: new 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
The rapid advancement and integration of large language models into specialized domains like time series analysis necessitate continuous refinement to address inherent data characteristics.
Improving the effectiveness of time series analysis with LLMs can unlock new capabilities in forecasting, anomaly detection, and predictive maintenance across numerous industries.
This research introduces a method to better constrain time series tokens for LLMs, likely leading to more accurate and robust models for complex temporal data.
- · AI/ML researchers
- · Industries relying on time series analysis (e.g., finance, manufacturing)
- · Developers of TS-LLMs
- · Traditional time series models (potentially less competitive)
- · LLM approaches that disregard data continuity and ordinality
Improved performance and broader adoption of token-based TS-LLMs.
Accelerated development of domain-specific LLMs for real-time data processing and decision-making.
Enhanced automation and predictive capabilities in critical infrastructure and dynamic systems, potentially increasing operational efficiencies and resilience.
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Read at arXiv cs.LG