University of Twente Student Theses
Applications of Explainable AI in Financial Time Series
Tirsi, H. (2024) Applications of Explainable AI in Financial Time Series.
Full text not available from this repository.
| Full Text Status: | Access to this publication is restricted |
| Embargo date: | 31 March 2026 |
| Abstract: | Deep learning models have evolved significantly, achieving high performance in forecasting. However, they are often referred to as black-box models because their decision-making process remains unclear. Furthermore, the AI Act in Europe requires AI models in high-risk categories to be more transparent. In this research, we applied TimeSHAP to a financial use case and investigated its open issues and research directions. Beyond explaining the machine learning model, this study also explores how to make the entire machine learning pipeline more explainable by incorporating ontologies based on the Explainable ML Pipeline Ontology (XMLPO) |
| Item Type: | Essay (Master) |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 01 general works, 85 business administration, organizational science |
| Programme: | Business Information Technology MSc (60025) |
| Link to this item: | https://purl.utwente.nl/essays/106100 |
| Export this item as: | BibTeX EndNote HTML Citation Reference Manager |
Show download statistics for this publication
Show download statistics for this publicationRepository Staff Only: item control page