Explainable transformers in financial forecasting

World Journal of Advanced Research and Reviews 20 (02):1434–1441 (2023)
  Copy   BIBTEX

Abstract

This study presents a novel transformer-based model specifically designed for financial forecasting, integrating explainability mechanisms such as SHAP (SHapley Additive exPlanations) values and attention visualizations to enhance interpretability. Unlike previous models, which often compromise between accuracy and transparency, our approach balances predictive accuracy with interpretability, allowing stakeholders to gain deeper insights into the factors driving market changes. By revealing critical market influences through feature importance and attention maps, this model provides both robustness and transparency, catering to the needs of high-stakes financial environments.

Analytics

Added to PP
2025-02-17

Downloads
476 (#63,007)

6 months
476 (#3,546)

Historical graph of downloads since first upload
This graph includes both downloads from PhilArchive and clicks on external links on PhilPapers.
How can I increase my downloads?