Towards Interpretable and Trustworthy Time Series Reasoning: A BlueSky Vision
It addresses the need for more reliable and understandable temporal analysis across diverse domains, but it is incremental as it builds on existing concepts without presenting new empirical results.
This paper tackles the problem of advancing time series reasoning beyond pattern recognition by proposing a vision for explicit, interpretable, and trustworthy inference, outlining a flexible framework that integrates robust foundations and system-level approaches.
Time series reasoning is emerging as the next frontier in temporal analysis, aiming to move beyond pattern recognition towards explicit, interpretable, and trustworthy inference. This paper presents a BlueSky vision built on two complementary directions. One builds robust foundations for time series reasoning, centered on comprehensive temporal understanding, structured multi-step reasoning, and faithful evaluation frameworks. The other advances system-level reasoning, moving beyond language-only explanations by incorporating multi-agent collaboration, multi-modal context, and retrieval-augmented approaches. Together, these directions outline a flexible and extensible framework for advancing time series reasoning, aiming to deliver interpretable and trustworthy temporal intelligence across diverse domains.