15 citations · 66 across the 32 of their papers we have counts for
6 papers · 2 filters
Volatility forecasting using Deep Learning and sentiment analysis
V Ncume, T. L van Zyl, A Paskaramoorthy
Several studies have shown that deep learning models can provide more accurate volatility forecasts than the traditional methods used within this domain. This paper presents a comp…
Towards a methodology for addressing missingness in datasets, with an application to demographic health datasets
Gift Khangamwa, Terence L. van Zyl, Clint J. van Alten
Missing data is a common concern in health datasets, and its impact on good decision-making processes is well documented. Our study's contribution is a methodology for tackling mis…
Using Machine Learning to Fuse Verbal Autopsy Narratives and Binary Features in the Analysis of Deaths from Hyperglycaemia
Thokozile Manaka, Terence Van Zyl, Alisha N Wade +1
Lower-and-middle income countries are faced with challenges arising from a lack of data on cause of death (COD), which can limit decisions on population health and disease manageme…
Evaluating State of the Art, Forecasting Ensembles- and Meta-learning Strategies for Model Fusion
Pieter Cawood, Terence van Zyl
Techniques of hybridisation and ensemble learning are popular model fusion techniques for improving the predictive power of forecasting methods. With limited research that instigat…
Statistics and Deep Learning-based Hybrid Model for Interpretable Anomaly Detection
Thabang Mathonsi, Terence L van Zyl
Hybrid methods have been shown to outperform pure statistical and pure deep learning methods at both forecasting tasks, and at quantifying the uncertainty associated with those for…
Investigating Transfer Learning in Graph Neural Networks
Nishai Kooverjee, Steven James, Terence van Zyl
Graph neural networks (GNNs) build on the success of deep learning models by extending them for use in graph spaces. Transfer learning has proven extremely successful for tradition…