---
title: "TEMPO: Time Series Understanding via Discrete Tokenization"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2611.04482v1"
permalink: "https://feed.astralor.com/items/itm_0a197937ef94485fbf3a7f31"
published: "2026-11-03T08:13:33.000Z"
score: 57
channel: "ai"
tags: ["LLM","TEMPO","Time Series Understanding","Discrete Tokenization Time-series","Integrating","Time-series"]
---

# TEMPO: Time Series Understanding via Discrete Tokenization

## 速览

Time-series interpretation often requires both numerical observations and textual context. Integrating these inputs into a pretrained language model raises a representation question: how should a signal enter the model'…

## 关键点

- Time-series interpretation often requires both numerical observations and textual context.
- Integrating these inputs into a pretrained language model raises a representation question: how should a signal enter the model's token sequence?
- We present TEMPO, which extends an LLM's vocabulary with discrete codes from a context-aware Transformer tokenizer using finite scalar quantization.

## 全文

Time-series interpretation often requires both numerical observations and textual context. Integrating these inputs into a pretrained language model raises a representation question: how should a signal enter the model's token sequence? We present TEMPO, which extends an LLM's vocabulary with discrete codes from a context-aware Transformer tokenizer using finite scalar quantization. A frozen tokenizer maps four-step patches to a 625-code vocabulary; signal tokens, scale statistics, and text are then processed by the LLM's existing self-attention layers. Adaptation updates the new token embeddings and DoRA adapters. With a Qwen3-4B backbone and task-specific fine-tuning, TEMPO achieves macro-F1 scores of 89.0% on activity recognition, 85.0% on sleep staging, and 86.0% on bearing-fault diagnosis. Time-series question answering and qualitative bearing-diagnosis examples further illustrate how this interface combines signal representations with textual context. Together, these results demonstrate the utility of discrete vocabulary extension for time-series understanding.

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