3 papers
cs.LG2026
SpikF-GO: Spiking Fourier Graph Operators for Multivariate Time Series Forecasting
Jafar Bakhshaliyev, Niels Landwehr
Spiking Neural Networks (SNNs) have emerged as an energy-efficient alternative to conventional neural networks, demonstrating strong performance in computer vision and robotics. Mo…
cs.LG2026
Temporal Patch Shuffle (TPS): Leveraging Patch-Level Shuffling to Boost Generalization and Robustness in Time Series Forecasting
Jafar Bakhshaliyev, Johannes Burchert, Niels Landwehr +1
Data augmentation is a crucial technique for improving model generalization and robustness, particularly in deep learning models where training data is limited. Although many augme…
cs.LG2024
Wave-Mask/Mix: Exploring Wavelet-Based Augmentations for Time Series Forecasting
Dona Arabi, Jafar Bakhshaliyev, Ayse Coskuner +2
Data augmentation is important for improving machine learning model performance when faced with limited real-world data. In time series forecasting (TSF), where accurate prediction…