Abstract
Most existing time series classification methods adopt a discriminative paradigm that maps input sequences directly to one-hot encoded class labels. While effective, this paradigm struggles to incorporate contextual features and fails to capture semantic relationships among classes. To address these limitations, we propose InstructTime, a novel framework that reformulates time series classification as a multimodal generative task. Specifically, continuous numerical sequences, contextual textual features, and task instructions are treated as multimodal inputs, while class labels are generated as textual outputs by tuned language models. To bridge the modality gap, InstructTime introduces a time series discretization module that converts continuous sequences into discrete temporal tokens, together with an alignment projection layer and a generative self-supervised pre-training strategy to enhance cross-modal representation alignment. Building upon this framework, we further propose InstructTime++, which extends InstructTime by incorporating implicit feature modeling to compensate for the limited inductive bias of language models. InstructTime++ leverages specialized toolkits to mine informative implicit patterns from raw time series and contextual inputs, including statistical feature extraction and vision-language-based image captioning, and translates them into textual descriptions for seamless integration. Extensive experiments on multiple benchmark datasets demonstrate the superior performance of InstructTime++.
Abstract (translated)
大多数现有的时间序列分类方法采用了一种判别式范式，该范式直接将输入序列映射为一个由one-hot编码表示的类别标签。尽管这种方法有效，但它难以整合上下文特征，并且无法捕捉类别的语义关系。为了克服这些限制，我们提出了InstructTime，这是一个新的框架，它重新定义时间序列分类为一个多模态生成任务。具体来说，在该框架中，连续数值序列、上下文文本特征和任务指令被视为多模态输入，而类别标签则通过调整过的语言模型生成为文本输出。 为了弥合不同模态之间的鸿沟，InstructTime引入了一个时间序列离散化模块，它将连续的时间序列转换成离散的时间标记。此外，还包括一个对齐投影层和一种增强跨模态表示对齐的生成自监督预训练策略。 在此框架的基础上，我们进一步提出了InstructTime++，该方法通过引入隐式特征建模来扩展InstructTime以弥补语言模型有限的归纳偏差。InstructTime++利用专门工具包从原始时间序列和上下文输入中挖掘出有用的隐式模式，包括统计特征提取以及基于视觉-语言的时间序列描述生成，并将这些模式转化为文本描述进行无缝集成。 在多个基准数据集上的广泛实验表明了InstructTime++的优越性能。
URL
https://arxiv.org/abs/2601.14968
