Interpretable Machine Learning for Financial Forecastin
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Abstract
Interpretable Machine Learning (IML) has gained significant attention in the realm of financial forecasting due to its capacity to provide transparent insights into complex models. This paper explores the application of IML techniques in financial forecasting, focusing on enhancing model transparency, accuracy, and risk assessment. We delve into the use of explainable models such as decision trees, rule-based systems, and LIME (Local Interpretable Model-Agnostic Explanations) for understanding the underlying patterns in financial data. Furthermore, we present a comparative analysis of IML methods against traditional black-box models, highlighting the advantages of interpretability in terms of regulatory compliance and risk management.
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