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Towards Interpretable and Trustworthy Time Series Reasoning: A BlueSky Vision

Kanghui Ning, Zijie Pan, Yushan Jiang, Anderson Schneider, Yuriy Nevmyvaka, Dongjin Song

TL;DR

This work formalizes time series reasoning as jointly predicting outputs $y$ and an interpretable reasoning path $\mathcal{R}$ from temporal data $\mathcal{X}=\{x_t\}_{t=1}^T$ with optional context $\mathcal{C}$, via $(y, \mathcal{R}) = f_\theta(\mathcal{X}, \mathcal{C}; \mathcal{T})$. It introduces a BlueSky vision with two complementary directions: robust foundations for temporal understanding, structured reasoning, and faithful evaluation, and system-level reasoning that integrates multi-agent collaboration, multi-modal context, and retrieval-augmented approaches. The paper argues for faithful evaluation, explainability, and grounding in domain knowledge to ensure trustworthy temporal inference. If realized, the framework promises more interpretable and reliable temporal intelligence across domains such as healthcare, finance, climate science, energy, and transportation.

Abstract

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.

Towards Interpretable and Trustworthy Time Series Reasoning: A BlueSky Vision

TL;DR

This work formalizes time series reasoning as jointly predicting outputs and an interpretable reasoning path from temporal data with optional context , via . It introduces a BlueSky vision with two complementary directions: robust foundations for temporal understanding, structured reasoning, and faithful evaluation, and system-level reasoning that integrates multi-agent collaboration, multi-modal context, and retrieval-augmented approaches. The paper argues for faithful evaluation, explainability, and grounding in domain knowledge to ensure trustworthy temporal inference. If realized, the framework promises more interpretable and reliable temporal intelligence across domains such as healthcare, finance, climate science, energy, and transportation.

Abstract

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.
Paper Structure (13 sections, 1 equation, 1 figure)

This paper contains 13 sections, 1 equation, 1 figure.

Table of Contents

  1. Introduction
  2. What is Time Series Reasoning?
  3. Why Time Series Reasoning?
  4. Our BlueSky Idea
  5. Build Robust Foundations for Time Series Reasoning
  6. Enabling Temporal Understanding
  7. Structured Reasoning
  8. Faithful Evaluation
  9. Beyond Language: Towards System-Level Time Series Reasoning
  10. Multi-Agent Collaboration
  11. Multi-Modal Context and Model
  12. Retrieval-Augmented Reasoning
  13. Conclusion

Figures (1)

  • Figure 1: Overview of the proposed BlueSky idea. Left: build robust foundations for time series reasoning, including temporal understanding, structured reasoning, and faithful evaluation. Right: extend beyond language, towards system-level time series reasoning, such as multi-agent collaboration, multi-modal context and model, and retrieval-augmented reasoning.