UCLA最新！大模型时序推理和Agentic系统的全面综述

Core Insights - The article discusses the emergence of Time Series Reasoning (TSR) as a new field that integrates large language models (LLMs) with time series data analysis, addressing the limitations of traditional methods [2][8][39] - TSR aims to enhance the capabilities of time series analysis by providing explicit reasoning, causal inference, and decision-making abilities, moving beyond mere prediction and classification [2][8][39] Summary by Sections Traditional Time Series Analysis Limitations - Traditional methods like ARIMA and LSTM excel in specific tasks but face three key limitations: lack of interpretability, inability to handle causal relationships, and insufficient dynamic responses [8][14] - LLMs offer new tools to overcome these limitations by providing explicit reasoning processes, generating causal hypotheses, and enabling interaction with external tools [2][8] Emergence of Time Series Reasoning - TSR is defined as the method of performing explicit structured reasoning on time-indexed data using LLMs, integrating multimodal contexts and agent systems [8][39] - A recent survey from a collaborative team outlines a clear definition of TSR and presents a three-dimensional classification framework covering reasoning structure, task objectives, and technical features [3][9] Three-Dimensional Classification Framework - The framework categorizes TSR into three dimensions: reasoning topology (how reasoning is conducted), core objectives (why reasoning is performed), and attribute labels (auxiliary features of methods) [9][24] - Reasoning topology includes three types: direct reasoning, linear chain reasoning, and branch-structured reasoning, each with varying complexity and capabilities [12][22] Reasoning Topology - Direct reasoning is the simplest form, providing results without showing intermediate steps, which limits interpretability [15] - Linear chain reasoning introduces ordered steps, enhancing interpretability and modularity [18] - Branch-structured reasoning allows for multiple paths and self-correction, increasing flexibility and adaptability [22] Core Objectives of Time Series Reasoning - The core objectives of TSR are categorized into four types: traditional time series analysis, explanation and understanding, causal inference and decision-making, and time series generation [24][28] - Each objective aims to enhance the performance and flexibility of traditional tasks through LLM integration [28] Attribute Labels - Attribute labels provide additional features for classifying methods, including control flow operations, execution agents, information sources, and LLM alignment methods [29][30] - These labels help researchers refine their work and understand the nuances of different approaches [29] Resources and Tools - The article emphasizes the importance of resources and tools for advancing the field, categorizing them into reasoning-first benchmarks, reasoning-ready benchmarks, and general-purpose benchmarks [33][36] - These resources are essential for researchers to test and validate their methodologies effectively [33] Future Directions and Challenges - The field faces several challenges, including standardizing evaluation metrics for reasoning quality, integrating multimodal data, and ensuring the robustness and safety of agent systems [38][39] - Addressing these challenges will define the future trajectory of time series reasoning, aiming for large-scale reliability in critical sectors like finance, healthcare, and energy [39]