Paper

FATE: Focal-modulated Attention Encoder for Multivariate Time-series Forecasting

arXiv:2408.11336v3 Announce Type: replace-cross Abstract: Climate change stands as one of the most pressing global challenges of the twenty-first century, with far-reaching consequences such as rising sea levels, melting glaciers, and increasingly extreme weather patterns. Accurate forecasting is critical for monitoring these phenomena and supporting mitigation strategies. While recent data-driven models for time-series forecasting, including CNNs, RNNs, and attention-based transformers, have shown promise, they often struggle with sequential dependencies and limited parallelization, especial…

Authors:

Related discussions