ConTSG-Bench: A Unified Benchmark for Conditional Time Series Generation
This benchmark addresses the lack of standardized evaluation for conditional time series generation models, which is a problem for researchers and practitioners needing to compare and develop new methods.
This paper introduces ConTSG-Bench, a unified benchmark for conditional time series generation, featuring a large-scale dataset with diverse conditioning modalities and semantic abstraction levels. It enables systematic evaluation of generative models using comprehensive metrics for fidelity and condition adherence, revealing limitations in current approaches regarding precise structural controllability and downstream task utility.
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.