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

ArXi:2602.23161v2 Announce Type: replace 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.