15 citations · 66 across the 32 of their papers we have counts for
14 papers · 1 filter
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…
Improving Cause-of-Death Classification from Verbal Autopsy Reports
Thokozile Manaka, Terence van Zyl, Deepak Kar
In many lower-and-middle income countries including South Africa, data access in health facilities is restricted due to patient privacy and confidentiality policies. Further, since…
Exploring the effectiveness of surrogate-assisted evolutionary algorithms on the batch processing problem
Mohamed Z. Variawa, Terence L. Van Zyl, Matthew Woolway
Real-world optimisation problems typically have objective functions which cannot be expressed analytically. These optimisation problems are evaluated through expensive physical exp…
Multi-Modal Recommendation System with Auxiliary Information
Mufhumudzi Muthivhi, Terence L. van Zyl, Hairong Wang
Context-aware recommendation systems improve upon classical recommender systems by including, in the modelling, a user's behaviour. Research into context-aware recommendation syste…
Pareto Driven Surrogate (ParDen-Sur) Assisted Optimisation of Multi-period Portfolio Backtest Simulations
Terence L. van Zyl, Matthew Woolway, Andrew Paskaramoorthy
Portfolio management is a multi-period multi-objective optimisation problem subject to a wide range of constraints. However, in practice, portfolio management is treated as a singl…
Knowledge Graph Fusion for Language Model Fine-tuning
Nimesh Bhana, Terence L. van Zyl
Language Models such as BERT have grown in popularity due to their ability to be pre-trained and perform robustly on a wide range of Natural Language Processing tasks. Often seen a…
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…
Surrogate Assisted Evolutionary Multi-objective Optimisation applied to a Pressure Swing Adsorption system
Liezl Stander, Matthew Woolway, Terence L. Van Zyl
Chemical plant design and optimisation have proven challenging due to the complexity of these real-world systems. The resulting complexity translates into high computational costs…
Fusion of Sentiment and Asset Price Predictions for Portfolio Optimization
Mufhumudzi Muthivhi, Terence L. van Zyl
The fusion of public sentiment data in the form of text with stock price prediction is a topic of increasing interest within the financial community. However, the research literatu…
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…
Deep Reinforcement Learning and Convex Mean-Variance Optimisation for Portfolio Management
Ruan Pretorius, Terence van Zyl
Traditional portfolio management methods can incorporate specific investor preferences but rely on accurate forecasts of asset returns and covariances. Reinforcement learning (RL)…
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…