Summary of A Survey Of Explainable Artificial Intelligence (xai) in Financial Time Series Forecasting, by Pierre-daniel Arsenault et al.
A Survey of Explainable Artificial Intelligence (XAI) in Financial Time Series Forecasting
by Pierre-Daniel Arsenault, Shengrui Wang, Jean-Marc Patenande
First submitted to arxiv on: 22 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The survey categorizes recent published work on explainable AI (XAI) approaches that predict financial time series. The study distinguishes between explainability and interpretability, emphasizing their importance in understanding AI models. It provides a comprehensive view of XAI’s role in finance through clear definitions, taxonomy, characterization, and examples of applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores the use of XAI in predicting financial time series. While AI models have achieved high accuracy, their complexity can lead to decreased human trust. The study highlights the importance of understanding AI models for high-risk decision-making domains like finance. It categorizes recent published work on XAI approaches and provides examples of applications in the finance industry. |
Keywords
» Artificial intelligence » Time series
