5 papers
CASC: Causal Adversarial Subspace Clustering for Multivariate Spatiotemporal Data
Francis Ndikum Nji, Vandana Janeja, Jianwu Wang
Deep subspace clustering plays a critical role in applications involving multivariate spatiotemporal data, such as sea ice monitoring, disease spread analysis, and tracking neuro-d…
TTCD:Transformer Integrated Temporal Causal Discovery from Non-Stationary Time Series Data
Omar Faruque, Sahara Ali, Xue Zheng +1
The widespread availability of complex time series data in various domains such as environmental science, epidemiology, and economics demands robust causal discovery methods that c…
FAConvLSTM: Factorized-Attention ConvLSTM for Efficient Feature Extraction in Multivariate Climate Data
Francis Ndikum Nji, Jianwu Wang
Learning physically meaningful spatiotemporal representations from high-resolution multivariate Earth observation data is challenging due to strong local dynamics, long-range telec…
Attention-Guided Deep Adversarial Temporal Subspace Clustering (A-DATSC) Model for multivariate spatiotemporal data
Francis Ndikum Nji, Vandana Janeja, Jianwu Wang
Deep subspace clustering models are vital for applications such as snowmelt detection, sea ice tracking, crop health monitoring, infectious disease modeling, network load predictio…
B-TGAT: A Bi-directional Temporal Graph Attention Transformer for Clustering Multivariate Spatiotemporal Data
Francis Ndikum Nji, Vandana Janaja, Jianwu Wang
Clustering high-dimensional multivariate spatiotemporal climate data is challenging due to complex temporal dependencies, evolving spatial interactions, and non-stationary dynamics…