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Orientador(es)
Resumo(s)
This master’s thesis investigates the Conformal Finance Pipeline with Visualization Dashboard, an artifact developed using the Design Science Research methodology to address uncertainty in financial forecasting. Empirical validation is conducted under both static-split and rolling-window (walk-forward) evaluation protocols. While the static-split method enables comprehensive hyperparameter testing across multiple test-window lengths (41–252 days) and crisis periods (2014, COVID-19, 2022 monetary tightening), the rolling-window protocol— aligned with the original ECI authors’ online adaptive framework—serves as the primary benchmark for real-world application, despite its higher computational demands. Results show that XGBoost and RandomForest consistently produce the narrowest valid intervals in regression tasks, while the classification pipeline achieves strong directional accuracy and well-calibrated prediction-set sizes. Conditional coverage analysis by volatility buckets and Winkler (WIS) scoring further supports the adaptive performance of the ECI-Integral and ECI-Cutoff variants across low-, medium-, and high-volatility regimes. The study also uncovers stable patterns in interval dynamics—particularly in Diff_qs_NCM and intraday-range ratios— that point to promising avenues for uncertainty-aware trading strategies. All results are integrated into a Power BI dashboard, providing analytical insights through prediction intervals, empirical coverage rates (ECR), average interval widths (AIW), and the Winkler Score (WIS), a proper scoring rule that penalizes both miscoverage and excessive width.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics
Palavras-chave
Uncertainty Quantification Conformal Prediction Dashboard ECI Finance
