arXiv cs.CL· Zhao Tan, Xianglong Bao, Zhipeng Xi, Ming Jin·· 13 小时前AI 评分29
TT-SAX：面向自然语言形状查询的两轨时间序列符号化方法
TT-SAX: A Two-Track Time Series Symbolization for Natural Language Shape Querying
AI 导读
TT-SAX 在 NLQTSBench 基准的形状查询任务上优于六种经典符号化方法，实验覆盖六款 LLM。该方法对 SAX 做最小改造：保留层级字母，为每个分段新增趋势字母，趋势在相邻分段之间测量并按查询所用形容词分级。作者指出经典符号化存在方向缺失、方向测量尺度过小、字母任意三项局限，消融实验逐项验证了增益来源。
正文 · 原文
Abstract:Natural language querying for time series databases aims to let users find what they need in long histories of data by asking in plain language. Shape queries ask how a series moves. They are the main challenge of this task. Recent work makes them feasible by using symbolization as a bridge between the query and the long history. However, existing symbolizations were designed for compression, similarity indexing, or classification. What a symbolization should provide for natural language querying has not been systematically studied. We therefore examine the definitions of six classical symbolizations against this task and identify three limitations. (1) Direction is missing from the string, (2) direction is measured at too small a scale, and (3) the letters are arbitrary. Three design principles follow. We follow these principles and make a minimal redesign of SAX called TT-SAX (two-track SAX). The level letters stay. Each segment gains a trend letter. It is measured across neighboring segments rather than inside one, and graded to match the adjectives that queries use. We conduct extensive experiments on the public NLQTSBench benchmark, comparing TT-SAX with the six classical symbolizations across six LLMs. TT-SAX outperforms all six on shape queries, and ablations trace its gains back to the three limitations one by one. This paper is the first systematic study of time series symbolization with natural language querying as the design goal. It provides a foundation for future research on this task. Our code is available at this https URL.
| Comments: | 14 pages, 8 figures, 2 tables. Accepted at the Australasian Database Conference (ADC 2026). This is the submitted manuscript prior to peer review |
| Subjects: | Databases (cs.DB); Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.30593 [cs.DB] |
| (or arXiv:2610.30593v1 [cs.DB] for this version) | |
| https://doi.org/10.48550/arXiv.2610.30593 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Zhao Tan [view email]
[v1]
Thu, 22 Oct 2026 01:49:10 UTC (519 KB)
来源：arXiv cs.CL · arxiv.org