AION: Next-Generation Tasks and Practical Harness for Time Series

2026-05-24

AI Summary

This work addresses the limitations of traditional time series methods, which are constrained by fixed forecasting horizons and struggle to support contextual reasoning, tool invocation, and structured decision-making in real-world scenarios. The authors propose AION, a framework that formalizes time series tasks as a triplet of task specification, workspace, and validation interface, integrating six core modules—agents, skills, rules, memory, evaluation, and protocols—to emphasize temporal grounding, knowledge-guided reasoning, and reliability assurance. By incorporating process traceability, multi-level auditing, and post-hoc experimental analysis, AION overcomes the constraints of static evaluation paradigms. In a Kaggle store sales forecasting case study, AION substantially outperforms direct modeling approaches, generating richer reasoning traces, intermediate artifacts, and audit steps, thereby demonstrating its effectiveness and superiority in handling complex, real-world time series tasks.

Abstract

Time series research is moving beyond fixed forecasting benchmarks toward realistic tasks that combine prediction, contextual reasoning, tool use, and structured decision support. Most benchmarks are built around clean data and short evaluation loops; agents alone may miss temporal constraints, evidence checks, or review before finalizing outputs. We first formalize next-generation time series tasks as three-component tuples consisting of a task file, a workspace, and a validation interface. We then present AION, a time series harness built from six component groups: agents, skills, rules, memory, evaluation, and protocols. In this harness, we use three design principles: temporal grounding, temporal knowledge-grounded reasoning, and reliability mechanisms such as post-experiment analysis and layered review. A Kaggle Store Sales case study shows that the harness produces more detailed process traces, more artifacts, and more review steps than the same base agent operating in OpenCode direct build mode. Taken together, these results argue for a paradigm shift from fixed tasks to realistic ones under real-world constraints.

Problem

• time series
• realistic tasks
• temporal constraints
• decision support
• benchmark limitations

Innovation

• time series
• agent harness
• temporal reasoning
• realistic tasks
• reliability mechanisms