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.

翻译：数据在数字金融生态系统中巩固市场、服务与产品方面发挥着基础性作用。然而，使用真实数据（尤其在金融背景下）可能引发隐私风险与访问限制，影响机构、研究及建模流程。尽管并非所有金融数据集都存在此类限制，本文提出采用深度学习技术生成应用于加密货币价格时间序列的合成数据。该方法基于条件生成对抗网络（CGAN），结合LSTM型递归生成器与MLP判别器，以生成统计一致的合成数据。实验涵盖不同加密资产，结果表明该模型能够复现相关时间模式，保留市场趋势与动态。通过GAN生成合成序列是模拟金融数据的有效替代方案，在市场规模行为分析与异常检测等应用中展现出潜力，且计算成本低于更复杂的生成方法。