VV
V. Vranceanu
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1
An Analysis of Visualisation Techniques for High-Dimensional Pareto Frontiers
A Case Study in Investment Portfolio Optimisation
To explore high-dimensional Pareto frontiers calculated using multi-objective optimisation, analysts rely on visualisation techniques ranging from general-purpose charts to purpose-built dashboards. Prior empirical comparisons were not able to select a single chart type as best practice. Additionally, dashboards are evaluated as complete systems, and no study covers investment portfolio optimisation. We address this gap through a study in which domain experts helped characterise the analytical tasks and served as an initial filter for the techniques we evaluate. We then assessed six visualisation techniques arranged in an interactive dashboard with 57 non-expert and 10 expert participants, across a single-frontier analysis and a frontier comparison on 3D and 6D datasets. The results show that the view choices depend on the task being solved, while no single technique dominates overall in usage or performance. Specialised views stand out on specific tasks, while the tables' role as a robust and easy-to-learn baseline is reinforced by the findings in this study. Our framework is available at: \url{https://github.com/Gunterinos/master_thesis}
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To explore high-dimensional Pareto frontiers calculated using multi-objective optimisation, analysts rely on visualisation techniques ranging from general-purpose charts to purpose-built dashboards. Prior empirical comparisons were not able to select a single chart type as best practice. Additionally, dashboards are evaluated as complete systems, and no study covers investment portfolio optimisation. We address this gap through a study in which domain experts helped characterise the analytical tasks and served as an initial filter for the techniques we evaluate. We then assessed six visualisation techniques arranged in an interactive dashboard with 57 non-expert and 10 expert participants, across a single-frontier analysis and a frontier comparison on 3D and 6D datasets. The results show that the view choices depend on the task being solved, while no single technique dominates overall in usage or performance. Specialised views stand out on specific tasks, while the tables' role as a robust and easy-to-learn baseline is reinforced by the findings in this study. Our framework is available at: \url{https://github.com/Gunterinos/master_thesis}
Graph Neural Networks for Long-Term Traffic Forecasting
Can GNNs effectively handle long-term predictions and how does their accuracy degrade over time?
Traffic forecasting is a branch of spatiotemporal forecasting that involves predicting future traffic speed or volume based on real-world data. It has a significant impact on urban mobility and quality of life, as it directly contributes to improving traffic management and trip planning. This study evaluates the performance of Graph Neural Networks (GNNs) in handling long-term forecasting, defined as predictions made up to 10 hours ahead. It addresses the evolution of performance and factors that may impact accuracy, such as fluctuations in traffic speed and road network configurations. The experiments are done using subsets of a benchmark dataset for traffic forecasting and a state-of-the-art GNN model. The findings showcase a logarithmic growth in prediction errors and the presence of two types of traffic jams—sudden and regular—along with their impact on prediction accuracy. Furthermore, the results highlight the complexity of quantifying the influence a factor has on forecasting performance, such as road network configuration or missing values.
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Traffic forecasting is a branch of spatiotemporal forecasting that involves predicting future traffic speed or volume based on real-world data. It has a significant impact on urban mobility and quality of life, as it directly contributes to improving traffic management and trip planning. This study evaluates the performance of Graph Neural Networks (GNNs) in handling long-term forecasting, defined as predictions made up to 10 hours ahead. It addresses the evolution of performance and factors that may impact accuracy, such as fluctuations in traffic speed and road network configurations. The experiments are done using subsets of a benchmark dataset for traffic forecasting and a state-of-the-art GNN model. The findings showcase a logarithmic growth in prediction errors and the presence of two types of traffic jams—sudden and regular—along with their impact on prediction accuracy. Furthermore, the results highlight the complexity of quantifying the influence a factor has on forecasting performance, such as road network configuration or missing values.