Explainable AI in Financial Forecasting Using Time Series Analysis

Abstract

Financial forecasting is a cornerstone of investment strategy, economic planning, and risk mitigation. With the advent of Artificial Intelligence (AI), models such as Long Short-Term Memory (LSTM) networks and other deep learning techniques have drastically improved forecasting accuracy. However, the lack of transparency in these models has raised concerns, particularly in regulatory and high-stakes environments. Explainable Artificial Intelligence (XAI) addresses this limitation by offering interpretability into model behavior and predictions. This paper investigates the integration of XAI methods—particularly SHapley Additive exPlanations (SHAP)—into time series forecasting models like LSTM and Facebook Prophet. We apply these models to real-world datasets, including stock indices and foreign exchange rates, comparing their predictive performance and interpretability. Results show that XAI-enhanced models maintain high forecasting accuracy while offering actionable insights, making them suitable for both technical analysts and financial regulators. The study highlights the importance of transparency in AI-driven decision systems and proposes a balanced approach between predictive power and explainability

Introduction

Financial forecasting is vital for investors, policymakers, and financial institutions to reduce risks and plan economically. Traditional statistical models like ARIMA and GARCH offer interpretability but struggle with the complex, nonlinear nature of real-world financial data. The rise of AI and deep learning models, particularly LSTM networks, has improved forecasting accuracy by capturing intricate patterns but at the cost of interpretability—these models act as “black boxes,” which complicates trust and regulatory compliance.

To address this, Explainable AI (XAI) techniques such as SHAP and LIME have been developed to clarify how AI models make predictions, enhancing transparency, trust, and adherence to regulations like the EU’s GDPR. Models like Facebook Prophet provide inherently interpretable forecasting through explicit trend and seasonality components, though sometimes less accurate than deep learning.

The study investigates integrating XAI (specifically SHAP) with LSTM models and compares them to Prophet and ARIMA to balance accuracy and explainability. Using financial datasets (e.g., S&P 500 and EUR/USD), the research finds that LSTM combined with SHAP delivers superior predictive performance while providing actionable explanations on feature importance, aiding stakeholder understanding and regulatory compliance. Prophet, while more interpretable by design, sacrifices some accuracy but remains valuable for transparency-focused applications.

The findings highlight the trade-offs between model accuracy and interpretability, the crucial role of domain expertise, and the potential of XAI to foster trust in AI-driven financial decisions. Challenges such as computational costs of SHAP and real-time application remain, but overall, explainable forecasting models represent a significant step toward accountable and effective financial AI systems.

Conclusion

In an era marked by increasingly complex financial markets and data-driven decision-making, the need for models that are both highly accurate and transparent is more critical than ever. This study has explored the convergence of Explainable Artificial Intelligence (XAI) and time series forecasting to address the dual challenge of predictive performance and model interpretability in the financial domain. By employing models such as Long Short-Term Memory (LSTM) networks and Facebook Prophet alongside explainability tools like SHapley Additive exPlanations (SHAP), we demonstrate that it is possible to construct systems that deliver robust forecasts while offering meaningful insights into their internal logic.The empirical results from our experiments, applied to real-world datasets such as the S&P 500 index and EUR/USD exchange rates, confirm that LSTM models equipped with SHAP explanations can outperform traditional and rule-based models in accuracy, while also achieving significant progress in interpretability. Likewise, Prophet offers a viable alternative when user transparency is paramount, even if it involves a slight trade-off in precision. These findings underscore a key theme of this research: that predictive accuracy and explainability need not be mutually exclusive, and that when used in tandem, advanced AI models and XAI frameworks can foster trust, compliance, and informed decision-making in financial environments.Moreover, the integration of XAI into financial forecasting has broader implications for governance, risk assessment, and ethical AI deployment. In regulatory contexts where transparency is mandated such as under the European Union’s General Data Protection Regulation (GDPR) or emerging frameworks on AI accountability explainable models offer a clear advantage. Institutions and investors are no longer content with opaque systems; they require tools that not only perform but also justify their predictions in a language comprehensible to humans. XAI fills this gap by turning AI models from black boxes into glass boxes, opening up the possibility of deeper stakeholder engagement and improved financial literacy.Nevertheless, this research acknowledges that the implementation of XAI methods still presents several challenges. There is an inherent trade-off between the complexity of models and the degree of interpretability achievable. While tools like SHAP are model-agnostic and powerful, they can be computationally intensive and may require expert understanding for accurate interpretation. Future innovations should focus on simplifying the interpretability process for non-technical users, enabling a broader range of stakeholders such as portfolio managers, compliance officers, and policy-makers to confidently interact with AI-driven systems.Looking ahead, this study lays the groundwork for several promising avenues of research. One potential direction is the development of hybrid models that combine multiple time series methods with real-time XAI dashboards, enhancing usability in high-frequency trading and automated risk assessment. Another opportunity lies in integrating causal inference techniques with XAI to explain not just how a model makes predictions, but why certain financial phenomena occur. Additionally, advancing research into human-AI collaboration in financial forecasting can help bridge the gap between automated systems and expert human judgment.In conclusion, the fusion of Explainable AI and time series forecasting represents a significant leap forward in the pursuit of intelligent, trustworthy, and user-friendly financial analytics. By balancing predictive precision with interpretative clarity, such models can empower decision-makers across the financial ecosystem to act with greater confidence, transparency, and accountability. The evolution of explainable forecasting systems will not only shape the future of financial modeling but also set new standards for ethical and responsible AI across domains.

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Copyright

Copyright © 2025 Dr. S. N. Arjun Kumar, Dr. R. Madhavi, Dr. M. Sundar Rajan . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.