Abstract
Time-series forecasting is a critical task across many domains, from engineering to economics, where accurate predictions drive strategic decisions. However, applying advanced deep learning models in challenging, volatile domains like finance is difficult due to the inherent limitation and dynamic nature of financial time series data. This scarcity often results in sub-optimal model training and poor generalization. The fundamental challenge lies in determining how to reliably augment scarce financial time series data to enhance the predictive accuracy of deep learning forecasting models. Our main contribution is a demonstration of how Generative Adversarial Networks (GANs) can effectively serve as a data augmentation tool to overcome data scarcity in the financial domain. Specifically, we show that training a Long Short-Term Memory (LSTM) forecasting model on a dataset augmented with synthetic data generated by a transformer-based GAN (TTS-GAN) significantly improves the forecasting accuracy compared to using real data alone. We confirm these results across different financial time series (Bitcoin and S\&P500 price data) and various forecasting horizons. Furthermore, we propose a novel, time series specific quality metric that combines Dynamic Time Warping (DTW) and a modified Deep Dataset Dissimilarity Measure (DeD-iMs) to reliably monitor the training progress and evaluate the quality of the generated data. These findings provide compelling evidence for the benefits of GAN-based data augmentation in enhancing financial predictive capabilities.
Abstract (translated)
时间序列预测在工程、经济等多个领域中是一项关键任务，准确的预测能够推动战略决策。然而，在金融等挑战性和波动性较强的领域中应用高级深度学习模型较为困难，因为这些领域的时序数据具有固有的局限性和动态特性，导致训练数据稀缺，进而影响到模型训练效果和泛化能力。核心问题在于如何可靠地增强稀疏的金融时间序列数据以提高深度学习预测模型的准确性。 我们的主要贡献是展示了生成对抗网络（GANs）可以有效地作为数据扩充工具来克服金融领域中的数据稀缺性。具体而言，我们表明通过使用基于Transformer的GAN（TTS-GAN）生成的合成数据增强的数据集训练长短期记忆网络（LSTM）预测模型，与仅使用真实数据相比，能够显著提高预测准确性。我们在不同金融时间序列（比特币和标普500价格数据）以及各种预测时长上都确认了这些结果。 此外，我们还提出了一种新的、特定于时间序列的质量度量标准，该标准结合动态时间规整（DTW）和改进的深度数据集差异测量方法（DeD-iMs），以可靠地监测训练进度并评估生成的数据质量。我们的发现为基于GAN的数据扩充在提升金融预测能力方面的优势提供了强有力的证据。
URL
https://arxiv.org/abs/2602.17865
