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cs.LG2026
One Step Closer to Ground Truth: A Multi-Scale Residual-Aware Representation Learning Pipeline for Predicting Time Series Data
Amrijit Biswas, Mustafa Kamal, Robin Krambroeckers +4
Transformer-based models have emerged as leading paradigms in time-series forecasting in recent years, employing self-attention mechanisms to capture long-range dependencies. Despi…
cs.LG2025
Temporal Window Smoothing of Exogenous Variables for Improved Time Series Prediction
Mustafa Kamal, Niyaz Bin Hashem, Robin Krambroeckers +2
Although most transformer-based time series forecasting models primarily depend on endogenous inputs, recent state-of-the-art approaches have significantly improved performance by…