15 citations · 66 across the 32 of their papers we have counts for
6 papers · 2 filters
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…
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…
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…