ConceptTS利用大型语言模型实现可解释的多变量时间序列预测，提供更直观的预测解释，适合需要理解预测因素的场景。与黑盒基线相比，准确性相当，值得一试。
ConceptTS是一种可解释的预测框架，利用大型语言模型提出相关概念和生成标签规则，将语言模型的领域知识转化为直接监督。该框架将预测组织在命名、可读的概念周围，包含描述历史背景、局部预测区间和完整预测范围的三种互补瓶颈。实验表明，ConceptTS在准确性上与强黑盒基线相当，同时产生具有语义意义的概念激活。
ConceptTS: LLM-Guided Concept Bottlenecks for Interpretable Multivariate Time-Series Forecasting
State-of-the-art multivariate time-series forecasters can model complex temporal and cross-variable dependencies, yet their opaque representations provide limited insight into why a particular forecast is produced. This lack of transparency restricts their use in settings where practitioners must understand and assess the factors underlying a prediction. We introduce ConceptTS, an interpretable forecasting framework that organizes its predictions around named, human-readable concepts. ConceptTS uses a large language model to propose task-relevant concepts and generate executable labeling rules, translating the language model's domain knowledge into direct supervision without costly manual concept annotation. The proposed concepts are organized into three complementary bottlenecks that describe the historical context, local forecast intervals, and the full forecast horizon. A shared decoder combines representations derived from their predicted activations to construct the forecast, making the model's decision process explicit and supporting direct concept-level interventions. Experiments on the Beijing Multi-Site Air Quality dataset show that ConceptTS achieves accuracy competitive with strong black-box baselines while producing semantically meaningful concept activations.