[2603.04767] ConTSG-Bench: A Unified Benchmark for Conditional Time Series Generation

Computer Science > Machine Learning arXiv:2603.04767 (cs) [Submitted on 5 Mar 2026] Title:ConTSG-Bench: A Unified Benchmark for Conditional Time Series Generation Authors:Shaocheng Lan, Shuqi Gu, Zhangzhi Xiong, Kan Ren View a PDF of the paper titled ConTSG-Bench: A Unified Benchmark for Conditional Time Series Generation, by Shaocheng Lan and 3 other authors View PDF HTML (experimental) Abstract: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. Comments: Subjects: Mach...