This work summarizes two strategies for completing time-series (TS) tasks
using today's language model (LLM): LLM-for-TS, design and train a fundamental
large model for TS data; TS-for-LLM, enable the pre-trained LLM to handle TS
data. Considering the insufficient data accumulation, limited resources, and
semantic context requirements, this work focuses on TS-for-LLM methods, where
we aim to activate LLM's ability for TS data by designing a TS embedding method
suitable for LLM. The proposed method is named TEST. It first tokenizes TS,
builds an encoder to embed them by instance-wise, feature-wise, and
text-prototype-aligned contrast, and then creates prompts to make LLM more open
to embeddings, and finally implements TS tasks. Experiments are carried out on
TS classification and forecasting tasks using 8 LLMs with different structures
and sizes. Although its results cannot significantly outperform the current
SOTA models customized for TS tasks, by treating LLM as the pattern machine, it
can endow LLM's ability to process TS data without compromising the language
ability. This paper is intended to serve as a foundational work that will
inspire further research.

本文总结了使用当今的语言模型（LLM）完成时间序列（TS）任务的两种策略：LLM-for-TS，设计和训练适用于 TS 数据的基础大模型；TS-for-LLM，使预训练的 LLM 能够处理 TS 数据。本文关注 TS-for-LLM 方法，旨在通过设计一种适用于 LLM 的 TS 嵌入方法来激活 LLM 对 TS 数据的处理能力。作者提出了一种名为 TEST 的方法，首先对 TS 进行分词，通过实例、特征和文本原型对齐等方式建立编码器进行嵌入，然后创建提示信息使 LLM 更加开放对嵌入的应用，并最终实现 TS 任务。实验证明，应用于 8 个具有不同结构和大小的 LLM 进行 TS 分类和预测任务，尽管结果无法显著超过针对 TS 任务定制的最先进模型，但将 LLM 视为模式机器能够赋予 LLM 处理 TS 数据的能力，而不损害其语言能力。本文旨在成为鼓励进一步研究的基础性工作。