mlsanityData to 7 Oct 2026

A Survey of Explainable Artificial Intelligence (XAI) in Financial Time Series Forecasting

arXiv 2407.15909 ↗First posted 22 Jul 2024cs.LG cs.AIcited by 14

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Abstract (arXiv)

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 the past five years, categorizes XAI approaches that predict financial time series. In this paper, 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 definitions, a rigorous taxonomy of XAI approaches, a complementary characterization, and examples of XAI's application in the finance industry, this paper provides a comprehensive view of XAI's current role in finance. It can also serve as a guide for selecting the most appropriate XAI approach for future applications.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 35 of its 137 references that are in the map. First posted on arXiv 22 Jul 2024.

Builds on

35 of 137 references.

References · 35

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23 more
  1. 2019Explainable Machine Learning in Deploymentcited by 210
  2. 2020Principles and Practice of Explainable Machine Learningcited by 99
  3. 2021Explaining Time Series Predictions with Dynamic Maskscited by 72
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 12.

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  1. 2025Forecasting VIX using interpretable Kolmogorov-Arnold networkscited by 3
  2. 2026GroupSegment-SHAP: Shapley Value Explanations with Group-Segment Players for Multivariate Time Seriescited by 1
  3. 2026Towards Self-Explainable Document Visual Question Answering with Chain-of-Explanation Predictionscited by 1
  4. 2026AION: Next-Generation Tasks and Practical Harness for Time Seriescited by 1
  5. 2025Guided by Stars: Interpretable Concept Learning Over Time Series via Temporal Logic Semanticscited by 0
  6. 2026ASGMamba: Adaptive Spectral Gating Mamba for Multivariate Time Series Forecastingcited by 0
  7. 2026Interpretability in Deep Time Series Models Demands Semantic Alignmentcited by 0
  8. 2026Exposing Vulnerabilities in Explanation for Time Series Classifiers via Dual-Target Attackscited by 0
  9. 2026KairosHope: A Next-Generation Time-Series Foundation Model for Specialized Classification via Dual-Memory Architecturecited by 0
  10. 2026INSIGHTS: Demonstration-Based Summaries of Time Series Predictorscited by 0
  11. 2026ChronoVAE-HOPE: Beyond Attention -- A Next-Generation VAE Foundation Model for Specialized Time Series Classificationcited by 0
  12. 2026AlgoEvolve: LLM-driven Meta-evolution of Algorithmic Trading Programscited by 0
2 more
  1. 2026Class-Aware Reinforcement Learning for Counterfactual Explanation Generationcited by 0
  2. 2026Traceable Multi-Agent System for Knowledge-Based Forecastingcited by 0