Fiaingen: A Financial Time Series Generative Method Matching Real-World Data Quality
Summary: arXiv:2510.01169v2 Announce Type: replace-cross
Introduction
In the realm of finance, data is a cornerstone that drives machine learning models, enabling significant advancements in both research and practical applications. The importance of accurate and robust models cannot be overstated, especially in the context of investment and trading decision-making. Yet, despite the abundance of available data, the quality and variety of real-world financial data remain limited. This scarcity of diverse financial asset data directly impacts the performance of machine learning models designed to facilitate trading and investment strategies.
The Challenge of Data Scarcity
The limitations associated with real-world financial data are a significant hurdle for researchers and practitioners alike. Traditional data acquisition methods often fail to provide sufficient quality or volume, leading to models that may not perform optimally. This data shortage calls for innovative solutions capable of generating synthetic data that closely resembles real-world scenarios.
Introducing Fiaingen
In response to this challenge, we introduce Fiaingen, a set of novel techniques for time series data generation. Fiaingen aims to produce synthetic financial data that can seamlessly integrate into existing machine learning frameworks. The effectiveness of these techniques is evaluated based on three key criteria:
- Overlap of real-world and synthetic data: This criterion assesses how closely the generated data mimics real-world data within a reduced dimensionality space.
- Performance on downstream machine learning tasks: Here, we evaluate how well models trained on synthetic data perform on actual financial tasks.
- Runtime performance: This measures the time taken to generate synthetic data, which is crucial for scalability.
Performance Evaluation
Our experiments demonstrate that the Fiaingen methods achieve state-of-the-art performance across all three evaluation criteria. The synthetic data generated using Fiaingen techniques closely mirrors the original time series data, thus providing a reliable alternative for training machine learning models. Additionally, the data generation process is remarkably efficient, with generation times typically in the range of seconds. This efficiency ensures that the approach is scalable and can be applied in various financial contexts.
Conclusion
As financial markets continue to evolve and data becomes increasingly critical to decision-making processes, the need for robust machine learning models becomes more pronounced. The Fiaingen methods represent a significant advancement in addressing the limitations of real-world data availability. By providing a means to generate high-quality synthetic data, Fiaingen not only enhances the training of machine learning models but also supports improved investment and trading strategies. Models trained on Fiaingen-generated data exhibit performance levels comparable to those trained on actual financial data, making it a promising tool for the finance industry.