8 papers
Deployment-Side Adaptiveness in Multi-Horizon Volatility Forecasting
Riku Green, Zahraa S. Abdallah, Telmo M Silva Filho
In financial forecasting, predictive performance depends not only on which model is trained, but also on how the trained model is deployed. We study this issue in multi-horizon vol…
Exposure Bias as Epistemic Underidentification in Recursive Forecasting
Riku Green, Zahraa S. Abdallah, Telmo M Silva Filho
Recursive multi-step forecasting is usually framed as distribution shift: models are trained on observed histories but deployed on their own predictions. We show this framing is in…
Expectations vs. Realities: The Cost of MSE-Optimal Forecasting Under Conditional Uncertainty
Riku Green, Zahraa S. Abdallah, Telmo M Silva Filho
Multi-step time series forecasting (MSF) is commonly evaluated using point-wise error metrics such as mean squared error (MSE), implicitly treating the conditional mean as a suffic…
Epistemic Error Decomposition for Multi-step Time Series Forecasting: Rethinking Bias-Variance in Recursive and Direct Strategies
Riku Green, Huw Day, Zahraa S. Abdallah +1
Multi-step forecasting is often described through a simple rule of thumb: recursive strategies are said to have high bias and low variance, while direct strategies are said to have…
Semantic Similarity in Radiology Reports via LLMs and NER
Beth Pearson, Ahmed Adnan, Zahraa S. Abdallah
Radiology report evaluation is a crucial part of radiologists' training and plays a key role in ensuring diagnostic accuracy. As part of the standard reporting workflow, a junior r…
TACTFL: Temporal Contrastive Training for Multi-modal Federated Learning with Similarity-guided Model Aggregation
Guanxiong Sun, Majid Mirmehdi, Zahraa Abdallah +3
Real-world federated learning faces two key challenges: limited access to labelled data and the presence of heterogeneous multi-modal inputs. This paper proposes TACTFL, a unified…