Continuity and Ordinality Matter: Constraining Time Series Tokens for Effective Time Series Analysis with Large Language Models

This paper introduces COM (Continuity and Ordinality Matter), a strategy that integrates geometric constraints into both the initialization and training stages of token-based time series large language models (TS-LLMs). The research demonstrates that preserving continuity and ordinality in time series token embeddings significantly improves model performance and generalizability.