[2510.01169] Fiaingen: A financial time series generative method matching real-world data quality

Computer Science > Machine Learning arXiv:2510.01169 (cs) [Submitted on 1 Oct 2025 (v1), last revised 24 Mar 2026 (this version, v2)] Title:Fiaingen: A financial time series generative method matching real-world data quality Authors:Jože M. Rožanec, Tina Žezlin, Laurentiu Vasiliu, Dunja Mladenić, Radu Prodan, Dumitru Roman View a PDF of the paper titled Fiaingen: A financial time series generative method matching real-world data quality, by Jo\v{z}e M. Ro\v{z}anec and Tina \v{Z}ezlin and Laurentiu Vasiliu and Dunja Mladeni\'c and Radu Prodan and Dumitru Roman View PDF HTML (experimental) Abstract:Data is vital in enabling machine learning models to advance research and practical applications in finance, where accurate and robust models are essential for investment and trading decision-making. However, real-world data is limited despite its quantity, quality, and variety. The data shortage of various financial assets directly hinders the performance of machine learning models designed to trade and invest in these assets. Generative methods can mitigate this shortage. In this paper, we introduce a set of novel techniques for time series data generation (we name them Fiaingen) and assess their performance across three criteria: (a) overlap of real-world and synthetic data on a reduced dimensionality space, (b) performance on downstream machine learning tasks, and (c) runtime performance. Our experiments demonstrate that the methods achieve state-of-the-art performance across ...