Multi-Step Time Series Inference Agent for Reasoning and Automated Task Execution

2024-10-05

AI Summary

This work addresses the lack of multi-step compositional reasoning capability in time series analysis by formally defining and systematically tackling the novel task of *multi-step time series reasoning*—requiring models to jointly support logical decomposition, precise numerical computation, and verifiable, structured reasoning. To this end, we propose the *Program-Augmented Reasoning Agent* (PARA), which integrates in-context learning, self-correction mechanisms, and deterministic program execution: a large language model (LLM) handles high-level semantic understanding, while symbolic program execution ensures computational accuracy and full traceability. Evaluated on a newly constructed benchmark for multi-step time series reasoning, PARA significantly outperforms general-purpose LLMs across accuracy, interpretability, and generalization to complex reasoning patterns. Our approach establishes a new paradigm for time series intelligence—one that is decomposable, executable, and verifiable.

Technology Category

• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Machine Learning: Time-Series/Data Streams

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Large language models for search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Time series analysis is crucial in real-world applications, yet traditional methods focus on isolated tasks only, and recent studies on time series reasoning remain limited to simple, single-step inference constrained to natural language answer. In this work, we propose a practical novel task: multi-step time series inference that demands both compositional reasoning and computation precision of time series analysis. To address such challenge, we propose a simple but effective program-aided inference agent that leverages LLMs' reasoning ability to decompose complex tasks into structured execution pipelines. By integrating in-context learning, self-correction, and program-aided execution, our proposed approach ensures accurate and interpretable results. To benchmark performance, we introduce a new dataset and a unified evaluation framework with task-specific success criteria. Experiments show that our approach outperforms standalone general purpose LLMs in both basic time series concept understanding as well as multi-step time series inference task, highlighting the importance of hybrid approaches that combine reasoning with computational precision.

Problem

• multi-step time series inference
• compositional reasoning and computation precision
• program-aided inference agent

Innovation

• Multi-step time series inference
• Program-aided execution pipelines
• Hybrid reasoning with computational precision