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Research Team

Current Posgraduate Students

  1. Thinawanga Tshisikhawe - Spatio-temporal Modelling of Wind Energy Data Using Bayesian Inference (PhD, begun 2024)

  2. Tshinanne Tshiololi-Uncovering Hidden Patterns in High-Frequency Financial Data: An Investigation into Deep Learning Architectures for Time Series Prediction (PhD, begun 2024)

  3. Richard A. Samuel-  Stochastic modelling of volatility, leverage effects, long-memory and extremal dependence of financial markets(PhD, begun 2020)

Former postgraduate students

  1. Thakhani Ravele - Probabilistic renewable energy modelling in South Africa (PhD graduated in 2024)

  2. Edina Chandiwana - Solar power forecasting using Gaussian process regression.
    (PhD, graduated in 2023).

  3. Ndava C. Mupondo - Modelling Zimbabwean stock market liquidity and volatility (PhD, graduated in 2022)

  4.  Rosinah M. Mukhodobwane - Modelling volatility, equity risk and extremal dependence of the BRICS stock markets. (PhD, graduated in 2021).

  5. Fhumulani I Mativha - Drought in Luvuvhu river catchment: Assessment, characterisation and forecasting. (PhD, graduated in 2020).

  6. Daniel Nheta - Entrepreneurship gaps framework: An investigation into expectations vs. realities (PhD, graduated in 2020)

  7. Predicting price volatility of cryptocurrency Ethereum. Vhukhudo R. Rambevha (MSc, Graduated in 2021)

  8. Hierarchical forecasting of monthly electricity demand. Ignitious Chauke (MSc, Graduated in 2022) 

  9. Forecasting wavelet de-noised global horizontal irradiance using attention-based long short-term memory network. Ndamulelo I. Nelwamondo (MSc, Graduated in 2022)

  10. Renewable energy forecasting in South Africa. Mamphaga Ratsilengo (MSc, Graduated in 2021)

  11. Forecasting hourly solar irradiance in South Africa using machine learning models. Tendani Mutavhatsindi (MSc, Graduated in 2020)

  12. Short-term wind power forecasting in South Africa using neural networks. Lucky Oghenechodja Daniel (MSc, Graduated in 2020)

  13. Forecasting hourly electricity demand in South Africa using machine learning models. Maduvhahafani Thanyani (MSc, Graduated in 2020) 

  14. Probabilistic solar power forecasting: An application to South African data. PhathutshedzoMpfumali (MSc, Graduated in 2019)

  15.  Modelling the extremal dependence structure of equity returns: A survey of four Africa equity markets. Richard Taiwo Abayomi Samuel (MSc, Graduated in 2019)

  16. Forecasting foreign direct investment in South Africa using nonparametric quantile regression models. Nyawedzeni Netshivhazwaulu (MSc, Graduated in 2019)

  17. Short-term load forecasting using quantile regression with an application to the unit commitment problem. Moshoko Emily Lebotsa (MSc, Graduated in 2018)

  18. Medium-term load forecasting using generalised additive models with tensor product interactions. Thakhani Ravele (MSc. Graduated in 2018)

  19. Stochastic modelling of daily peak electricity demand using extreme value theory. Jerry Boano Danquah (MSc, Graduated in 2018)

  20. Short-term hourly load forecasting in South Africa using neural networks. Elvis Tshiani Ilunga (MSc, Graduated in 2018)

  21. Modelling annual flood heights of the Limpopo River at Beitbridge border post using extreme value theory. Robert Kajambeu (MSc, Graduated in 2017)

  22. Modelling temperature in South Africa using extreme value theory. Murendeni Maurel Nemukula (MSc, Graduated in 2017)

  23. Modelling short-term probabilistic electricity demand in South Africa. Molete Mokhele (MSC, Graduated in 2016)

Advice to PhD Applicants

 To all students intending to do  PhD studies at the University of Venda under my supervision. 

 Please make sure you satisfy all the criteria below: 

  1.  You must have completed a masters’ degree in statistics, applied statistics, mathematical statistics, or data science. Must have published (or accepted for publication) at least one article in an accredited journal. 

  2. You should have obtained the equivalent of at least an upper second-class honours in statistics and merit (at least 70% at masters level). 

  3.  Sound knowledge of multivariate calculus, optimisation and matrix algebra. 

  4. You should be capable of programming with,  R  and/or Python.

  5. It is essential that you are familiar with reproducible research practices. 

  6. I work on probabilistic forecasting, time series, computational statistics, exploratory data analysis with applications in energy (load and renewable (solar and wind) ) and environmental systems.  If you have the background described above, then send me an email at  including your CV, a transcript of your results and a concept note not exceeding 5 pages. 

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