Explainable AI For the Brazilian Stock Market Index: A Post-Hoc Approach to Deep Learning Models in Time-Series Forecasting - Morais, Lucas Rabelo de Araujo; Fragoso, Gabriel Arnaud de Melo; Ludermir, Teresa Bernarda; Monteiro, Claudio Luis Alves (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), 2024)
Time-series forecasting is challenging when data lacks clear trends or seasonality, making traditional statistical models less effective. Deep Learning models, like Neural Networks, excel at capturing non-linear patterns and offer a promising alternative. The Bovespa Index (Ibovespa), a key indicator of Brazil’s stock market, is volatile, leading to potential investor losses due to inaccurate forecasts and limited market insight. Neural Networks can enhance forecast accuracy, but reduce model explainability. This study aims to use Deep Learning to forecast the Ibovespa, striving to balance high forecasting accuracy with model interpretability, to improve decision-making in time-series forecasting and provide valuable insights into the economic landscape of Brazil