Osama Ahmad

Osama Ahmad, Omer Abdul Jalil, Usman Nazir et al.

In the realm of applications where data dynamically evolves across spatial and temporal dimensions, Graph Neural Networks (GNNs) are often complemented by sequence modeling architectures, such as RNNs and transformers, to effectively model temporal changes. These hybrid models typically arrange the spatial and temporal learning components in series. A pioneering effort to jointly model the spatio-temporal dependencies using only GNNs was the introduction of the Block Adjacency Matrix \(\mathbf{A_B}\) \cite{1}, which was constructed by diagonally concatenating adjacency matrices from graphs at different time steps. This approach resulted in a single graph encompassing complete spatio-temporal data; however, the graphs from different time steps remained disconnected, limiting GNN message-passing to spatially connected nodes only. Addressing this critical challenge, we propose a novel end-to-end learning architecture specifically designed to mend the temporal dependencies, resulting in a well-connected graph. Thus, we provide a framework for the learnable representation of spatio-temporal data as graphs. Our methodology demonstrates superior performance on benchmark datasets, such as SurgVisDom and C2D2, surpassing existing state-of-the-art graph models in terms of accuracy. Our model also achieves significantly lower computational complexity, having far fewer parameters than methods reliant on CLIP and 3D CNN architectures.

Osama Ahmad, Lukas Wesemann, Fabian Waschkowski et al.

This paper focuses on spatiotemporal (ST) traffic prediction using graph neural networks (GNNs). Given that ST data comprises non-stationary and complex temporal patterns, interpreting and predicting such trends is inherently challenging. Representing ST data in decomposed modes helps infer underlying behavior and assess the impact of noise on predictive performance. We propose a framework that decomposes ST data into interpretable modes using variational mode decomposition (VMD) and processes them through a neural network for future state forecasting. Unlike existing graph-based traffic forecasters that operate directly on raw or aggregated time series, the proposed hybrid approach, termed the Variational Mode Graph Convolutional Network (VMGCN), first decomposes non-stationary signals into interpretable variational modes by determining the optimal mode count via reconstruction-loss minimization and then learns both intramode and cross-mode spatiotemporal dependencies through a novel attention-augmented GCN. Additionally, we analyze the significance of each mode and the effect of bandwidth constraints on multi-horizon traffic flow predictions. The proposed two-stage design yields significant accuracy gains while providing frequency-level interpretability with demonstrated superior performance on the LargeST dataset for both short-term and long-term forecasting tasks. The implementation is publicly available on https://github.com/OsamaAhmad369/VMGCN.

Osama Ahmad, Zubair Khalid

This paper focuses on improving the robustness of spatiotemporal long-term prediction using a variational mode graph convolutional network (VMGCN) by introducing 3D channel attention. The deep learning network for this task relies on historical data inputs, yet real-time data can be corrupted by sensor noise, altering its distribution. We model this noise as independent and identically distributed (i.i.d.) Gaussian noise and incorporate it into the LargeST traffic volume dataset, resulting in data with both inherent and additive noise components. Our approach involves decomposing the corrupted signal into modes using variational mode decomposition, followed by feeding the data into a learning pipeline for prediction. We integrate a 3D attention mechanism encompassing spatial, temporal, and channel attention. The spatial and temporal attention modules learn their respective correlations, while the channel attention mechanism is used to suppress noise and highlight the significant modes in the spatiotemporal signals. Additionally, a learnable soft thresholding method is implemented to exclude unimportant modes from the feature vector, and a feature reduction method based on the signal-to-noise ratio (SNR) is applied. We compare the performance of our approach against baseline models, demonstrating that our method achieves superior long-term prediction accuracy, robustness to noise, and improved performance with mode truncation compared to the baseline models. The code of the paper is available at https://github.com/OsamaAhmad369/VMGCN.

Osama Ahmad, Lukas Wesemann, Fabian Waschkowski et al.

Accurate spatiotemporal forecasting is critical for numerous complex systems but remains challenging due to complex volatility patterns and spectral entanglement in conventional graph neural networks (GNNs). While decomposition-integrated approaches like variational mode graph convolutional network (VMGCN) improve accuracy through signal decomposition, they suffer from computational inefficiency and manual hyperparameter tuning. To address these limitations, we propose the mode adaptive graph network (MAGN) that transforms iterative variational mode decomposition (VMD) into a trainable neural module. Our key innovations include (1) an unfolded VMD (UVMD) module that replaces iterative optimization with a fixed-depth network to reduce the decomposition time (by 250x for the LargeST benchmark), and (2) mode-specific learnable bandwidth constraints (αk ) adapt spatial heterogeneity and eliminate manual tuning while preventing spectral overlap. Evaluated on the LargeST benchmark (6,902 sensors, 241M observations), MAGN achieves an 85-95% reduction in the prediction error over VMGCN and outperforms state-of-the-art baselines.

Osama Ahmad, Zubair Khalid, Muhammad Tahir et al.

Monitoring air pollution is crucial for protecting human health from exposure to harmful substances. Traditional methods of air quality monitoring, such as ground-based sensors and satellite-based remote sensing, face limitations due to high deployment costs, sparse sensor coverage, and environmental interferences. To address these challenges, this paper proposes a framework for high-resolution spatiotemporal Air Quality Index (AQI) mapping using sparse sensor data, satellite imagery, and various spatiotemporal factors. By leveraging Graph Neural Networks (GNNs), we estimate AQI values at unmonitored locations based on both spatial and temporal dependencies. The framework incorporates a wide range of environmental features, including meteorological data, road networks, points of interest (PoIs), population density, and urban green spaces, which enhance prediction accuracy. We illustrate the use of our approach through a case study in Lahore, Pakistan, where multi-resolution data is used to generate the air quality index map at a fine spatiotemporal scale.