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http://hdl.handle.net/1893/38410| Appears in Collections: | Computing Science and Mathematics Conference Papers and Proceedings |
| Peer Review Status: | Refereed |
| Author(s): | Takahashi, Osamu Cuyt, Annie O'Hare, Anthony Lee, Wen-shin Ferrara, Massimiilinao |
| Contact Email: | wen-shin.lee@stir.ac.uk |
| Title: | An Explainable Estimation Model for Financial Time Series |
| Editor(s): | Vantaggi, Barbara Coletti, Giulianella Denoeux, Thierry Laurent, Anne Petturiti, Davide Miranda, Enrique Medina, Jesus Bouchon-Meunier, Bernadette Yager, Ronald R |
| Citation: | Takahashi O, Cuyt A, O'Hare A, Lee W & Ferrara M (2026) An Explainable Estimation Model for Financial Time Series. In: Vantaggi B, Coletti G, Denoeux T, Laurent A, Petturiti D, Miranda E, Medina J, Bouchon-Meunier B & Yager RR (eds.) <i>21st International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems Short Paper Proceedings</i>. The 21st International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2026), Sapienza University of Rome, Rome, Italy, 15.06.2026-19.06.2026, pp. 241-245. |
| Issue Date: | 15-Jun-2026 |
| Date Deposited: | 19-Oct-2026 |
| Conference Name: | The 21st International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2026) |
| Conference Dates: | 2026-06-15 - 2026-06-19 |
| Conference Location: | Sapienza University of Rome, Rome, Italy |
| Abstract: | Recent developments in artificial intelligence have led to powerful machine learning approaches to model fluctuating dynamics in financial time series, even in the presence of non-stationary behavior. However, such methods often suffer from limited interpretability while uncertainty, arising from complex data fluctuations, remains difficult to quantify. We present a method that decomposes financial data into different frequency components and trends, thereby enabling explicit identification of cyclical patterns and long-term trends. Concurrently, noise components are separated from signal components. Its effectiveness and its high predictive accuracy have been demonstrated in the interpretation of some financial time series. |
| Status: | VoR - Version of Record |
| Rights: | The papers published in this proceedings are —unless indicated otherwise— covered by the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 International (CC-BY- ND 3.0). You may copy, distribute, and transmit the work, provided that you attribute it (authorship, proceedings, publisher) in the manner specified by the author(s) or licensor(s), and that you do not use it for commercial purposes. The full text of the licence may be found at https://creativecommons.org/licenses/by-nc-nd/3.0/deed.en. |
| URL: | https://zenodo.org/records/20623312 |
| Licence URL(s): | http://creativecommons.org/licenses/by-nc-nd/3.0/ |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| IPMU2026_SHORT_PAPER_PROCEEDINGS-241-245.pdf | Fulltext - Published Version | 443.1 kB | Adobe PDF | View/Open |
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