6 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…
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…
An Empirical Evaluation of Factors Affecting SHAP Explanation of Time Series Classification
Davide Italo Serramazza, Nikos Papadeas, Zahraa Abdallah +1
Explainable AI (XAI) has become an increasingly important topic for understanding and attributing the predictions made by complex Time Series Classification (TSC) models. Among att…
Stratify: Unifying Multi-Step Forecasting Strategies
Riku Green, Grant Stevens, Zahraa Abdallah +1
A key aspect of temporal domains is the ability to make predictions multiple time steps into the future, a process known as multi-step forecasting (MSF). At the core of this proces…