8 citations · 23 across the 24 of their papers we have counts for
13 papers · 1 filter
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…
A Regime-Aware Fusion Framework for Time Series Classification
Honey Singh Chauhan, Zahraa S. Abdallah
Kernel-based methods such as Rocket are among the most effective default approaches for univariate time series classification (TSC), yet they do not perform equally well across all…
Establishing Validity for Distance Functions and Internal Clustering Validity Indices in Correlation Space
Isabella Degen, Zahraa S Abdallah, Kate Robson Brown +1
Internal clustering validity indices (ICVIs) assess clustering quality without ground truth labels. Comparative studies consistently find that no single ICVI outperforms others acr…
CSTS: A Benchmark for the Discovery of Correlation Structures in Time Series Clustering
Isabella Degen, Zahraa S Abdallah, Henry W J Reeve +1
Time series clustering promises to uncover hidden structural patterns in data with applications across healthcare, finance, industrial systems, and other critical domains. However,…
KnowEEG: Explainable Knowledge Driven EEG Classification
Amarpal Sahota, Navid Mohammadi Foumani, Raul Santos-Rodriguez +1
Electroencephalography (EEG) classification has shown significant promise in applications ranging from neurodegenerative disease detection to brain-computer interfaces (BCIs). Whil…
Time-Series Classification for Dynamic Strategies in Multi-Step Forecasting
Riku Green, Grant Stevens, Telmo de Menezes e Silva Filho +1
Multi-step forecasting (MSF) in time-series, the ability to make predictions multiple time steps into the future, is fundamental to almost all temporal domains. To make such foreca…
RED CoMETS: An ensemble classifier for symbolically represented multivariate time series
Luca A. Bennett, Zahraa S. Abdallah
Multivariate time series classification is a rapidly growing research field with practical applications in finance, healthcare, engineering, and more. The complexity of classifying…
Temporal patterns in insulin needs for Type 1 diabetes
Isabella Degen, Zahraa S. Abdallah
Type 1 Diabetes (T1D) is a chronic condition where the body produces little or no insulin, a hormone required for the cells to use blood glucose (BG) for energy and to regulate BG…
IMG-NILM: A Deep learning NILM approach using energy heatmaps
Jonah Edmonds, Zahraa S. Abdallah
Energy disaggregation estimates appliance-by-appliance electricity consumption from a single meter that measures the whole home's electricity demand. Compared with intrusive load m…
Co-eye: A Multi-resolution Symbolic Representation to TimeSeries Diversified Ensemble Classification
Zahraa S. Abdallah, Mohamed Medhat Gaber
Time series classification (TSC) is a challenging task that attracted many researchers in the last few years. One main challenge in TSC is the diversity of domains where time serie…
DeepStreamCE: A Streaming Approach to Concept Evolution Detection in Deep Neural Networks
Lorraine Chambers, Mohamed Medhat Gaber, Zahraa S. Abdallah
Deep neural networks have experimentally demonstrated superior performance over other machine learning approaches in decision-making predictions. However, one major concern is the…
Effect of Hyper-Parameter Optimization on the Deep Learning Model Proposed for Distributed Attack Detection in Internet of Things Environment
Md Mohaimenuzzaman, Zahraa Said Abdallah, Joarder Kamruzzaman +1
This paper studies the effect of various hyper-parameters and their selection for the best performance of the deep learning model proposed in [1] for distributed attack detection i…