# PATRA: Pattern-Aware Alignment and Balanced Reasoning for Time Series Question Answering

arXiv: https://arxiv.org/abs/2602.23161 · HTML: https://mlsanity.com/paper/2602.23161/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Time series reasoning, one of 17 directions in LLM reasoning.
- Builds on 13 of its 121 references that are in the map.
- First posted on arXiv 26 Feb 2026.

## Placed in

- LLM reasoning / [Time series reasoning](https://mlsanity.com/lr/time-series-reasoning/)

## Builds on (most cited first, 13 of 13)

- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [iTransformer: Inverted Transformers Are Effective for Time Series Forecasting](https://mlsanity.com/paper/2310.06625/) (2023)
- [Gemini: A Family of Highly Capable Multimodal Models](https://mlsanity.com/paper/2312.11805/) (2023)
- [Seed1.5-Thinking: Advancing Superb Reasoning Models with Reinforcement Learning](https://mlsanity.com/paper/2504.13914/) (2025)
- [Robust Watermarking Using Generative Priors Against Image Editing: From Benchmarking to Advances](https://mlsanity.com/paper/2410.18775/) (2024)
- [Follow-Your-Creation: Empowering 4D Creation through Video Inpainting](https://mlsanity.com/paper/2506.04590/) (2025)
- [VisMem: Latent Vision Memory Unlocks Potential of Vision-Language Models](https://mlsanity.com/paper/2511.11007/) (2025)
- [Visual Document Understanding and Reasoning: A Multi-Agent Collaboration Framework with Agent-Wise Adaptive Test-Time Scaling](https://mlsanity.com/paper/2508.03404/) (2025)
- [TimeOmni-1: Incentivizing Complex Reasoning with Time Series in Large Language Models](https://mlsanity.com/paper/2509.24803/) (2025)
- [Visual Multi-Agent System: Mitigating Hallucination Snowballing via Visual Flow](https://mlsanity.com/paper/2509.21789/) (2025)
- [MSHyper: Multi-Scale Hypergraph Transformer for Long-Range Time Series Forecasting](https://mlsanity.com/paper/2401.09261/) (2024)
- [ERNIE 5.0 Technical Report](https://mlsanity.com/paper/2602.04705/) (2026)
- [Deep Learning for Energy Time-Series Analysis and Forecasting](https://mlsanity.com/paper/2306.09129/) (2023)

## Built on by (most cited first, 4 of 4)

- [Decoupling Perception from Description: Computation-Grounded Representation Alignment between Multivariate Time Series and Language](https://mlsanity.com/paper/2608.05238/) (2026)
- [QiYao-M: Multimodal Time Series Foundation Model with Role-Aware Modeling of Endogenous and Exogenous Modalities](https://mlsanity.com/paper/2609.34842/) (2026)
- [XMatch: Enhancing Covariate-Aware Time Series Forecasting through Tree-Structured Exogenous Matching](https://mlsanity.com/paper/2609.34939/) (2026)
- [Do Time-Series QA Systems Read the Time Series? Evidence Use and Reasoning Reliability](https://mlsanity.com/paper/2610.05686/) (2026)

## Abstract (arXiv)

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.