用生成模型合成加密货币价格数据，解决真实数据隐私与获取难题。
Synthetic data in cryptocurrencies using generative models
- 基于条件GAN与LSTM生成器、MLP判别器生成合成价格序列。
- 能有效复现时间模式、市场趋势与动态特征，统计特性一致。
- 适合金融建模、异常检测等场景，计算成本低于复杂生成方法。
数据在数字金融生态系统中对市场、服务和产品整合起基础作用。然而，使用真实金融数据（尤其是加密货币）可能引发隐私风险与访问限制，影响机构、研究与建模过程。本文提出利用深度学习技术生成合成数据，应用于加密货币价格时间序列。方法基于条件生成对抗网络（CGAN），采用LSTM型循环生成器与MLP判别器，生成在统计上一致的合成数据。实验涵盖多种加密资产，结果表明模型能够有效再现关键的时间模式，保留市场趋势与动态特征。通过GAN生成合成序列是模拟金融数据的高效替代方案，具有潜在应用价值，如市场行为分析与异常检测，且相比更复杂的生成方法具有更低的计算成本。
原文摘要 · Abstract (English)
Data plays a fundamental role in consolidating markets, services, and products in the digital financial ecosystem. However, the use of real data, especially in the financial context, can lead to privacy risks and access restrictions, affecting institutions, research, and modeling processes. Although not all financial datasets present such limitations, this work proposes the use of deep learning techniques for generating synthetic data applied to cryptocurrency price time series. The approach is based on Conditional Generative Adversarial Networks (CGANs), combining an LSTM-type recurrent generator and an MLP discriminator to produce statistically consistent synthetic data. The experiments consider different crypto-assets and demonstrate that the model is capable of reproducing relevant temporal patterns, preserving market trends and dynamics. The generation of synthetic series through GANs is an efficient alternative for simulating financial data, showing potential for applications such as market behavior analysis and anomaly detection, with lower computational cost compared to more complex generative approaches.
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