Brenwen Ntlangu | University of Cape Town (original) (raw)
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Papers by Brenwen Ntlangu
Proceedings of the ICA, May 16, 2018
In South Africa, a team of analysts has for some years been using statistical techniques to predi... more In South Africa, a team of analysts has for some years been using statistical techniques to predict election outcomes during election nights in South Africa. The prediction method involves using statistical clusters based on past voting patterns to predict final election outcomes, using a small number of released vote counts. With the US presidential elections in November 2016 hitting the global media headlines during the time period directly after successful predictions were done for the South African elections, the team decided to investigate adapting their method to forecast the final outcome in the US elections. In particular, it was felt that the time zone differences between states would affect the time at which results are released and thereby provide a window of opportunity for doing election night prediction using only the early results from the eastern side of the US. Testing the method on the US presidential elections would have two advantages: it would determine whether the core methodology could be generalised, and whether it would work to include a stronger spatial element in the modelling, since the early results released would be spatially biased due to time zone differences. This paper presents a high-level view of the overall methodology and how it was adapted to predict the results of the US presidential elections. A discussion on the clustering of spatial units within the US is also provided and the spatial distribution of results together with the Electoral College prediction results from both a 'test-run' and the final 2016 presidential elections are given and analysed.
Modelling of network traffic is a notoriously difficult problem. This is primarily due to the eve... more Modelling of network traffic is a notoriously difficult problem. This is primarily due to the ever-increasing complexity of network traffic and the different ways in which a network may be excited by user activity. The ongoing development of new network applications, protocols, and usage profiles further necessitate the need for models which are able to adapt to the specific networks in which they are deployed. These considerations have in large part driven the evolution of statistical profiles of network traffic from simple Poisson processes to non-Gaussian models that incorporate traffic burstiness, non-stationarity, self-similarity, long-range dependence (LRD) and multi-fractality. The need for ever more sophisticated network traffic models has led to the specification of a myriad of traffic models since. Many of these are listed in [91, 14]. In networks comprised of IoT devices much of the traffic is generated by devices which function autonomously and in a more deterministic fa...
With the advent of Internet of Things (IoT) technology, the need for tools which facillitate the ... more With the advent of Internet of Things (IoT) technology, the need for tools which facillitate the development and management of network based services has become increasingly important. Issues such as network security and quality of service are no longer just the concern of ISPs and big corporations, but have now become the intimate concern of private users with implications that directly affect the personal lives and businesses of citizens. At the heart of addressing these concerns lies the problem of modelling the networks on which interconnected devices now operate. While the activity of provisioning the networks and responding to threats may still lie in the hands of networking specialists, the ability to at least know when something is amiss remains intrumental to establishing the confidence and peace of mind of IoT users. In this paper we broadly review the historical development of network traffic modelling and trace a path that leads to the use of time series analysis for the...
Proceedings of the ICA, May 16, 2018
In South Africa, a team of analysts has for some years been using statistical techniques to predi... more In South Africa, a team of analysts has for some years been using statistical techniques to predict election outcomes during election nights in South Africa. The prediction method involves using statistical clusters based on past voting patterns to predict final election outcomes, using a small number of released vote counts. With the US presidential elections in November 2016 hitting the global media headlines during the time period directly after successful predictions were done for the South African elections, the team decided to investigate adapting their method to forecast the final outcome in the US elections. In particular, it was felt that the time zone differences between states would affect the time at which results are released and thereby provide a window of opportunity for doing election night prediction using only the early results from the eastern side of the US. Testing the method on the US presidential elections would have two advantages: it would determine whether the core methodology could be generalised, and whether it would work to include a stronger spatial element in the modelling, since the early results released would be spatially biased due to time zone differences. This paper presents a high-level view of the overall methodology and how it was adapted to predict the results of the US presidential elections. A discussion on the clustering of spatial units within the US is also provided and the spatial distribution of results together with the Electoral College prediction results from both a 'test-run' and the final 2016 presidential elections are given and analysed.
Modelling of network traffic is a notoriously difficult problem. This is primarily due to the eve... more Modelling of network traffic is a notoriously difficult problem. This is primarily due to the ever-increasing complexity of network traffic and the different ways in which a network may be excited by user activity. The ongoing development of new network applications, protocols, and usage profiles further necessitate the need for models which are able to adapt to the specific networks in which they are deployed. These considerations have in large part driven the evolution of statistical profiles of network traffic from simple Poisson processes to non-Gaussian models that incorporate traffic burstiness, non-stationarity, self-similarity, long-range dependence (LRD) and multi-fractality. The need for ever more sophisticated network traffic models has led to the specification of a myriad of traffic models since. Many of these are listed in [91, 14]. In networks comprised of IoT devices much of the traffic is generated by devices which function autonomously and in a more deterministic fa...
With the advent of Internet of Things (IoT) technology, the need for tools which facillitate the ... more With the advent of Internet of Things (IoT) technology, the need for tools which facillitate the development and management of network based services has become increasingly important. Issues such as network security and quality of service are no longer just the concern of ISPs and big corporations, but have now become the intimate concern of private users with implications that directly affect the personal lives and businesses of citizens. At the heart of addressing these concerns lies the problem of modelling the networks on which interconnected devices now operate. While the activity of provisioning the networks and responding to threats may still lie in the hands of networking specialists, the ability to at least know when something is amiss remains intrumental to establishing the confidence and peace of mind of IoT users. In this paper we broadly review the historical development of network traffic modelling and trace a path that leads to the use of time series analysis for the...