发表机构
University of Oxford; Vortexa（牛津大学; Vortexa）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对时间序列预测中解释生成难且易幻觉的问题，提出领域无关框架，含提取解释因素、基于证据生成解释及可扩展评估三组件，经案例研究验证，该框架能大规模实现有根据的解释生成，无需特定领域微调。
AI 中文摘要
时间序列预测广泛用于决策关键领域，通常需要附带解释。生成这些解释通常是手动且成本高昂的过程，使用大语言模型自动化时应用于时间数据常出现幻觉。我们提出了一个用于时间序列预测的有根据的自然语言解释生成的领域无关框架。该框架由三个组件组成：从历史分析师编写的解释中提取结构化解释因素、基于证据的解释生成以及对可读性、逻辑一致性和说服力的可扩展评估。设计明确将生成限制在可验证证据上，减少无根据的断言。我们在涉及纳斯达克100指数的金融预测案例研究和使用Vortexa数据的货运定价案例研究中评估了该框架。结果表明，生成的解释在可读性、一致性和说服力方面接近分析师编写的解释。这些发现表明，无需特定领域微调即可大规模实现时间序列预测的有根据的解释生成。
英文摘要
Time series forecasts are widely used in decision-critical domains, where they are rarely consumed without accompanying explanations. Producing such explanations is usually a manual and costly process, and attempts to automate it using large language models often suffer from hallucination when applied to temporal data. We propose a domain-agnostic framework for grounded natural language explanation generation for time series forecasts, illustrated in Figure 1. The framework consists of three components: (i) extraction of structured explanatory factors from historical analyst-written explanations, (ii) evidence-conditioned explanation generation, and (iii) scalable evaluation for readability, logical consistency, and persuasiveness. The design explicitly constrains generation to verifiable evidence, reducing unsupported claims. We evaluate the framework on a financial forecasting case study involving the NASDAQ-100 index and a freight pricing case study using data from Vortexa. Results show that generated explanations approached analyst-written explanations in terms of readability, consistency and persuasiveness. These findings demonstrate that grounded explanation generation for time series forecasting can be achieved at scale without domain-specific fine-tuning.
