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.
Abstract (translated)
合成时间序列是数据增强、压力测试和算法原型开发在量化金融中的重要工具。然而，在加密货币市场，其特点是24/7交易、极端波动性和快速的市场变化下，现有的时间序列生成（TSG）方法和基准往往无法满足需求，这削弱了其实用性。大多数之前的工作要么针对非金融或传统金融市场，要么仅聚焦于分类和预测而忽视了加密货币市场的特定复杂性，再者缺乏关键性的财务评估，尤其是对于交易应用的评估。 为了解决这些不足，我们引入了\textsf{CTBench}——首个专门面向加密货币领域的时间序列生成基准。该基准基于来自452种代币的开源数据集，并从13项指标对TSG模型进行评价，涵盖了五个关键维度：预测准确性、排名保真度、交易表现、风险评估和计算效率。其中一项创新在于双任务评估框架： - \emph{Predictive Utility}（预测效用）任务衡量合成数据在保留时间序列和横截面模式方面的效果。 - \emph{Statistical Arbitrage}（统计套利）任务评估重构的时间序列是否支持用于交易的均值回复信号。 我们对来自五种方法学派系的八个代表性模型进行了四个不同市场环境下的基准测试，揭示了统计保真度与现实世界盈利能力之间的权衡。特别地，\textsf{CTBench}提供了模型排名分析和在加密货币分析及策略开发中选择和部署TSG模型的实际指导。
URL
https://arxiv.org/abs/2508.02758
