标题： 时间序列语言模型用于描述性标题生成 显示英文标题

标题： Time Series Language Model for Descriptive Caption Generation

摘要： 时间序列数据中可观察模式的代表性自然语言描述的自动生成增强了可解释性，简化了分析，并提高了时间数据的跨领域实用性。 尽管预训练基础模型在自然语言处理（NLP）和计算机视觉（CV）方面取得了显著进展，但数据稀缺性阻碍了它们在时间序列分析中的应用。 虽然已经提出了几种基于大型语言模型（LLM）的时间序列预测方法，但在LLM背景下，时间序列字幕生成仍研究不足。 在本文中，我们介绍了TSLM，这是一种专为时间序列字幕设计的新颖时间序列语言模型。 TSLM作为一个编码器-解码器模型运行，利用文本提示和时间序列数据表示来捕捉多个阶段的细微时间模式，并生成对时间序列输入的精确文本描述。 TSLM通过首先利用上下文提示合成数据生成，其次通过一种新颖的跨模态密集检索评分对时间序列-字幕对进行去噪，解决了时间序列字幕生成中的数据稀缺问题。 在各种时间序列字幕数据集上的实验结果表明，TSLM在多个数据模态下显著优于现有的最先进方法。 显示英文摘要

摘要： The automatic generation of representative natural language descriptions for observable patterns in time series data enhances interpretability, simplifies analysis and increases cross-domain utility of temporal data. While pre-trained foundation models have made considerable progress in natural language processing (NLP) and computer vision (CV), their application to time series analysis has been hindered by data scarcity. Although several large language model (LLM)-based methods have been proposed for time series forecasting, time series captioning is under-explored in the context of LLMs. In this paper, we introduce TSLM, a novel time series language model designed specifically for time series captioning. TSLM operates as an encoder-decoder model, leveraging both text prompts and time series data representations to capture subtle temporal patterns across multiple phases and generate precise textual descriptions of time series inputs. TSLM addresses the data scarcity problem in time series captioning by first leveraging an in-context prompting synthetic data generation, and second denoising the generated data via a novel cross-modal dense retrieval scoring applied to time series-caption pairs. Experimental findings on various time series captioning datasets demonstrate that TSLM outperforms existing state-of-the-art approaches from multiple data modalities by a significant margin.