构建首个统一的条件时间序列生成评估基准
ConTSG-Bench: A Unified Benchmark for Conditional Time Series Generation
- 设计跨模态、多抽象层级的统一数据集
- 首次实现多种生成方法在结构可控性上的系统评测
- 适合研究生成模型可解释性与下游任务应用的学者
条件时间序列生成在解决现实应用中的数据稀缺问题和因果分析方面至关重要。尽管其重要性日益凸显，该领域仍缺乏标准化、系统的评估框架。为此，我们提出条件时间序列生成基准（ConTSG-Bench），包含大规模、对齐良好的数据集，覆盖多种条件模态和语义抽象层次，首次实现了在这些维度上对代表性生成方法的系统评估，并提供涵盖生成保真度与条件遵循度的综合指标体系。定量评估与深入行为分析揭示了现有方法的特性与局限，突出了精确结构控制能力和复杂条件下下游任务效用等关键挑战与有前景的研究方向。
原文摘要 · Abstract (English)
Conditional time series generation plays a critical role in addressing data scarcity and enabling causal analysis in real-world applications. Despite its increasing importance, the field lacks a standardized and systematic benchmarking framework for evaluating generative models across diverse conditions. To address this gap, we introduce the Conditional Time Series Generation Benchmark (ConTSG-Bench). ConTSG-Bench comprises a large-scale, well-aligned dataset spanning diverse conditioning modalities and levels of semantic abstraction, first enabling systematic evaluation of representative generation methods across these dimensions with a comprehensive suite of metrics for generation fidelity and condition adherence. Both the quantitative benchmarking and in-depth analyses of conditional generation behaviors have revealed the traits and limitations of the current approaches, highlighting critical challenges and promising research directions, particularly with respect to precise structural controllability and downstream task utility under complex conditions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。