15 citations · 66 across the 32 of their papers we have counts for
10 papers · 1 filter
A Statistics and Deep Learning Hybrid Method for Multivariate Time Series Forecasting and Mortality Modeling
Thabang Mathonsi, Terence L. van Zyl
Hybrid methods have been shown to outperform pure statistical and pure deep learning methods at forecasting tasks and quantifying the associated uncertainty with those forecasts (p…
Multivariate Anomaly Detection based on Prediction Intervals Constructed using Deep Learning
Thabang Mathonsi, Terence L. van Zyl
It has been shown that deep learning models can under certain circumstances outperform traditional statistical methods at forecasting. Furthermore, various techniques have been dev…
Incremental Class Learning using Variational Autoencoders with Similarity Learning
Jiahao Huo, Terence L. van Zyl
Catastrophic forgetting in neural networks during incremental learning remains a challenging problem. Previous research investigated catastrophic forgetting in fully connected netw…
AMA-K: Aggressive Multi-Temporal Allocation An Algorithm for Aggressive Online Portfolio Selection
Matthew Kruger, Terence L. van Zyl, Andrew Paskaramoorthy
Online portfolio selection is an integral componentof wealth management. The fundamental undertaking is tomaximise returns while minimising risk given investor con-straints. We aim…
Surrogate Parameters Optimization for Data and Model Fusion of COVID-19 Time-series Data
Timilehin Ogundare, Terence Van Zyl
Our research focuses on developing a computational framework to simulate the transmission dynamics of COVID-19 pandemic. We examine the development of a system named ADRIANA for th…
Surrogate Assisted Strategies (The Parameterisation of an Infectious Disease Agent-Based Model)
Rylan Perumal, Terence L van Zyl
Parameter calibration is a significant challenge in agent-based modelling and simulation (ABMS). An agent-based model's (ABM) complexity grows as the number of parameters required…
Feature-weighted Stacking for Nonseasonal Time Series Forecasts: A Case Study of the COVID-19 Epidemic Curves
Pieter Cawood, Terence L. van Zyl
We investigate ensembling techniques in forecasting and examine their potential for use in nonseasonal time-series similar to those in the early days of the COVID-19 pandemic. Deve…
ParDen: Surrogate Assisted Hyper-Parameter Optimisation for Portfolio Selection
Terence van Zyl, Matthew Woolway, Andrew Paskaramoorthy
Portfolio optimisation is a multi-objective optimisation problem (MOP), where an investor aims to optimise the conflicting criteria of maximising a portfolio's expected return whil…
The efficient frontiers of mean-variance portfolio rules under distribution misspecification
Andrew Paskaramoorthy, Tim Gebbie, Terence van Zyl
Mean-variance portfolio decisions that combine prediction and optimisation have been shown to have poor empirical performance. Here, we consider the performance of various shrinkag…
Deep Similarity Learning for Sports Team Ranking
Daniel Yazbek, Jonathan Sandile Sibindi, Terence L. Van Zyl
Sports data is more readily available and consequently, there has been an increase in the amount of sports analysis, predictions and rankings in the literature. Sports are unique i…