{"ID":2887393,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.02758","arxiv_id":"2508.02758","title":"CTBench: Cryptocurrency Time Series Generation Benchmark","abstract":"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 \\textsf{CTBench}, the first comprehensive TSG benchmark tailored for the cryptocurrency domain. \\textsf{CTBench} curates an open-source dataset from 452 tokens and evaluates TSG models across 13 metrics spanning 5 key dimensions: forecasting accuracy, rank fidelity, trading performance, risk assessment, and computational efficiency. A key innovation is a dual-task evaluation framework: (1) the \\emph{Predictive Utility} task measures how well synthetic data preserves temporal and cross-sectional patterns for forecasting, while (2) the \\emph{Statistical Arbitrage} task assesses whether reconstructed series support mean-reverting signals for trading. We benchmark eight representative models from five methodological families over four distinct market regimes, uncovering trade-offs between statistical fidelity and real-world profitability. Notably, \\textsf{CTBench} offers model ranking analysis and actionable guidance for selecting and deploying TSG models in crypto analytics and strategy development.","short_abstract":"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 sh...","url_abs":"https://arxiv.org/abs/2508.02758","url_pdf":"https://arxiv.org/pdf/2508.02758v1","authors":"[\"Yihao Ang\",\"Qiang Wang\",\"Qiang Huang\",\"Yifan Bao\",\"Xinyu Xi\",\"Anthony K. H. Tung\",\"Chen Jin\",\"Zhiyong Huang\"]","published":"2025-08-03T17:07:08Z","proceeding":"q-fin.ST","tasks":"[\"q-fin.ST\",\"cs.AI\",\"cs.CE\",\"cs.DB\",\"cs.LG\"]","methods":"[]","has_code":false}