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[2602.17865] Financial time series augmentation using transformer based GAN architecture
Summary
This article explores the use of transformer-based GANs for augmenting financial time series data, enhancing predictive accuracy in forecasting models.
Why It Matters
Financial time series forecasting is crucial for decision-making in economics and finance. This research addresses the challenge of data scarcity in this volatile domain by demonstrating how GANs can effectively augment data, leading to improved model performance. This has significant implications for financial analysts and data scientists seeking to enhance their forecasting capabilities.
Key Takeaways
- GANs can effectively augment limited financial time series data.
- Using synthetic data improves the accuracy of LSTM forecasting models.
- A novel quality metric for evaluating generated data is proposed.
- Results are validated across different financial datasets, including Bitcoin and S&P 500.
- The research highlights the potential of deep learning in finance despite data challenges.
Computer Science > Machine Learning arXiv:2602.17865 (cs) [Submitted on 19 Feb 2026] Title:Financial time series augmentation using transformer based GAN architecture Authors:Andrzej Podobiński, Jarosław A. Chudziak View a PDF of the paper titled Financial time series augmentation using transformer based GAN architecture, by Andrzej Podobi\'nski and 1 other authors View PDF HTML (experimental) 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 financ...
