González Cortés, DanielOnieva Caracuel, EnriquePastor López, IkerTrinchera, LauraWu, Jian2025-05-202025-05-202024-02-05Cortes, D. G., Onieva, E., Lopez, I. P., Trinchera, L., & Wu, J. (2024). Autoencoder-enhanced clustering: a dimensionality reduction approach to financial time series. IEEE Access, 12, 16999-17009. https://doi.org/10.1109/ACCESS.2024.335941310.1109/ACCESS.2024.3359413https://hdl.handle.net/20.500.14454/2782While Machine Learning significantly boosts the performance of predictive models, its efficacy varies across different data dimensions. It is essential to cluster time series data of similar characteristics, particularly in the financial sector. However, clustering financial time series data poses considerable challenges due to the market's inherent complexity and multidimensionality. To address these issues, our study introduces a novel clustering framework that leverages autoencoders for a compressed yet informative representation of financial time series. We rigorously evaluate our approach through multiple dimensionality reduction and clustering algorithms, applying it to key financial indices, including IBEX-35, CAC-40, DAX-30, S&P 500, and FTSE 100. Our findings consistently demonstrate that incorporating autoencoders significantly enhances the granularity and quality of clustering, effectively isolating distinct categories of financial time series. Our findings carry significant ramifications for the financial industry. By refining clustering methodologies, we set the stage for increasingly accurate financial predictive models, offering valuable insights for optimizing investment strategies and enhancing risk management.eng© 2024 The AuthorsClustering methodsData compressionFinancial data processingNeural network applicationsTime seriesjournal article2025-05-202169-3536