A Survey of Explainable Artificial Intelligence (XAI) in Financial Time Series Forecasting
Université de Sherbrooke · Espace pour la vie
Abstract
Artificial intelligence (AI) models have reached a very significant level of accuracy. While their superior performance offers considerable benefits, their inherent complexity often decreases human trust, which slows their application in high-risk decision-making domains, such as finance. The field of explainable AI (XAI) seeks to bridge this gap, aiming to make AI models more understandable. This survey, focusing on published work from 2018 to 2024, categorizes XAI approaches that predict financial time series. In this article, explainability and interpretability are distinguished, emphasizing the need to treat these concepts separately, as they are not applied the same way in practice. Through clear…
Citation impact
- FWCI
- 71.42
- Percentile
- 100%
- References
- 125
Authors
3Topics & keywords
- Computer science
- Series (stratigraphy)
- Artificial intelligence
- Time series
- Finance
- Machine learning
- Business