2 citations · 4 across the 11 of their papers we have counts for
6 papers · 1 filter
ChaosNetBench: Benchmarking Spatio-Temporal Graph Neural Networks on Chaotic Lattice Dynamics
Henok Tenaw Moges, Charalampos Skokos, Deshendran Moodley
Spatio-temporal graph neural networks (STGNNs) are widely used for short-term forecasting in dynamic physical systems such as traffic and weather. However, the prevailing evaluatio…
A lightweight Spatial-Temporal Graph Neural Network for Long-term Time Series Forecasting
Henok Tenaw Moges, Deshendran Moodley
We propose Lite-STGNN, a lightweight spatial-temporal graph neural network for long-term multivariate forecasting that integrates decomposition-based temporal modeling with learnab…
An analysis of deep neural networks for predicting trends in time series data
Kouame Hermann Kouassi, Deshendran Moodley
Recently, a hybrid Deep Neural Network (DNN) algorithm, TreNet was proposed for predicting trends in time series data. While TreNet was shown to have superior performance for trend…
Automatic deep learning for trend prediction in time series data
Kouame Hermann Kouassi, Deshendran Moodley
Recently, Deep Neural Network (DNN) algorithms have been explored for predicting trends in time series data. In many real world applications, time series data are captured from dyn…
Clustering Residential Electricity Consumption Data to Create Archetypes that Capture Household Behaviour in South Africa
Wiebke Toussaint, Deshendran Moodley
Clustering is frequently used in the energy domain to identify dominant electricity consumption patterns of households, which can be used to construct customer archetypes for long…
Using competency questions to select optimal clustering structures for residential energy consumption patterns
Wiebke Toussaint, Deshendran Moodley
During cluster analysis domain experts and visual analysis are frequently relied on to identify the optimal clustering structure. This process tends to be adhoc, subjective and dif…