# ConTSG-Bench: A Unified Benchmark for Conditional Time Series Generation

arXiv: https://arxiv.org/abs/2603.04767 · HTML: https://mlsanity.com/paper/2603.04767/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2026.

## Built on by (most cited first, 4 of 4)

- [What if Tomorrow is the World Cup Final? Counterfactual Time Series Forecasting with Textual Conditions](https://mlsanity.com/paper/2605.14422/) (2026)
- [Exploring the Potential of Probabilistic Transformer for Time Series Modeling: A Report on the ST-PT Framework](https://mlsanity.com/paper/2604.26762/) (2026)
- [Represent, Then Generate: Multimodal-Conditioned Time-Series Generation under Irregular Missingness](https://mlsanity.com/paper/2608.12592/) (2026)
- [GALA: Generation-Aware Cross-Modal Alignment for Text-to-Time-Series Synthesis](https://mlsanity.com/paper/2608.13741/) (2026)

## Abstract (arXiv)

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.