让大模型读懂时间序列中的趋势与周期，提升复杂问题推理能力。
PATRA: Pattern-Aware Alignment and Balanced Reasoning for Time Series Question Answering
- 通过提取趋势和季节性模式，实现时间序列与问题的深度对齐。
- 在多任务训练中平衡难易任务影响，显著提升复杂推理表现。
- 适合需要深度时序分析的金融、气象等领域研究者使用。
时间序列推理需要同时具备对复杂动态的感知能力和逻辑深度。然而，现有基于大模型的方法存在两方面局限：一是将时间序列简单视为文本或图像，未能捕捉回答特定问题所需的趋势、季节性等模式；二是在混合简单与复杂任务的训练中，简单目标常主导学习过程，抑制深层推理能力的发展。为此，我们提出模式感知对齐与平衡推理模型（PATRA），引入模式感知机制，从时间序列中提取趋势与季节性模式以实现深度对齐；同时设计任务感知的平衡奖励机制，协调不同难度任务间的训练，激励生成连贯的思维链。大量实验表明，PATRA在多种时间序列问答（TSQA）任务上均优于强基线，展现出卓越的跨模态理解与推理能力。
原文摘要 · Abstract (English)
Time series reasoning demands both the perception of complex dynamics and logical depth. However, existing LLM-based approaches exhibit two limitations: they often treat time series merely as text or images, failing to capture the patterns like trends and seasonalities needed to answer specific questions; and when trained on a mix of simple and complex tasks, simpler objectives often dominate the learning process, hindering the development of deep reasoning capabilities. To address these limitations, we propose the Pattern-Aware Alignment and Balanced Reasoning model (PATRA), introducing a pattern-aware mechanism that extracts trend and seasonality patterns from time series to achieve deep alignment. Furthermore, we design a task-aware balanced reward to harmonize learning across tasks of varying difficulty, incentivizing the generation of coherent Chains of Thought. Extensive experiments show that PATRA outperforms strong baselines across diverse Time Series Question Answering (TSQA) tasks, demonstrating superior cross-modal understanding and reasoning capability.
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