让时间序列预测同时生成解释，一次输出因果推理与数值结果。
ReasonCast: Towards Explainable Time Series Forecasting with Reasoning
- 用统一架构联合生成预测和解释性文本，避免任务分离
- 在ReasonTS-Bench上实现比纯预测模型更高的准确率
- 适合需要可解释预测的金融、医疗等场景
现有时间序列模型多专精于单一任务：理解（以文本回答）或生成（输出数值预测）。尽管近期出现统一架构，仍采用任务分路处理，无法在同一响应中同时给出预测及其原因。本文提出任务融合模型ReasonCast，联合生成预测与自解释文本，实现数值预测与可验证因果推理的一体化输出。为此构建了ReasonTS-Bench基准，识别出五种时间序列基本模式，支持双任务联合评估。通过微调任意大语言模型，ReasonCast可在单次自回归生成中完成推理链与预测输出。大量实验表明，其预测精度超越主流大模型与专用时间序列模型，且推理过程具备可验证性。代码已开源：https://github.com/seunghan96/reasoncast。
原文摘要 · Abstract (English)
Most time series (TS) models are specialized for a single task, either understanding (i.e., returning text answers about a TS) or generation (i.e., returning a numeric forecast). Only recently have unified models begun to handle the two within a single architecture. Even these models, however, produce the two outputs as task-separated paths and cannot predict a series and explain why that prediction arises within a single coherent response. In this paper, we argue for a task-fused model that jointly produces 1) prediction (generation) and 2) selfexplanation (understanding), thereby integrating 1) numerical TS forecasting and 2) interpretable text reasoning within a single response. To enable the systematic study of this capability, we present both a benchmark and a recipe that jointly address the two tasks. The benchmark, ReasonTS-Bench, identifies five fundamental patterns underlying TS and enables the joint evaluation of both tasks. ReasonCast, our recipe for finetuning any LLM to perform both tasks jointly, yields a model that generates a reasoning chain and a forecast together in a single autoregressive pass. Extensive experiments show that ReasonCast outperforms both LLMs and TS models on prediction accuracy while producing verifiable, causal reasoning. Code is available at: https://github.com/seunghan96/reasoncast.
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