CTBench: Cryptocurrency Time Series Generation Benchmark

Synthetic time series are essential tools for data augmentation, stress testing, and algorithmic prototyping in quantitative finance. However, in cryptocurrency markets, characterized by 24/7 trading, extreme volatility, and rapid regime shifts, existing Time Series Generation (TSG) methods and benchmarks often fall short, jeopardizing practical utility. Most prior work (1) targets non-financial or traditional financial domains, (2) focuses narrowly on classification and forecasting while neglecting crypto-specific complexities, and (3) lacks critical financial evaluations, particularly for trading applications. To address these gaps, we introduce \textbf{CTBench}, the first \textbf{C}rypto-centric \textbf{T}ime series generation \textbf{Bench}mark, comprising (1) an open dataset of 452 cryptocurrencies, (2) a dual-task evaluation on Predictive Utility and Statistical Arbitrage, and (3) 13 metrics across six dimensions (i.e., error, rank, trading performance, risk assessment, efficiency, and visualization). We systematically benchmark 8 representative TSG models from five families, uncovering trade-offs between statistical fidelity and real-world profitability. Notably, CTBench offers model ranking analysis and actionable guidance for selecting and deploying TSG models in crypto analytics and strategy development.