让时间序列模型更好理解数据的连续性和顺序性，提升分析效果。
Continuity and Ordinality Matter: Constraining Time Series Tokens for Effective Time Series Analysis with Large Language Models
- 在初始化和训练中加入几何约束，保持时间序列的连续与顺序。
- 多个基准测试显示性能显著提升，泛化能力更强。
- 适合需要精准时序推理的工业、金融场景研究者使用。
基于标记的时间序列大语言模型（TS-LLMs）已成为时间序列分析与推理的有前景方向。然而，先前研究大多忽视了时间序列标记固有的连续性与序数性，严重限制了模型性能。本文认为，在时间序列标记嵌入中保留这些特性对基于标记的TS-LLMs的有效性至关重要。为此，我们提出COM（Continuity and Ordinality Matter）策略，将几何约束整合至初始化与训练阶段。在多个时间序列分析基准上的实验证明，COM持续提升了基于标记的TS-LLMs性能，达到具有竞争力的结果并具备强泛化能力。代码已公开于 https://anonymous.4open.science/r/COM。
原文摘要 · Abstract (English)
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 https://anonymous.4open.science/r/COM .
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。