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

• URL: http://arxiv.org/abs/2408.11336v2
• Date: Sun, 15 Jun 2025 07:50:57 GMT
• Title: FATE: Focal-modulated Attention Encoder for Multivariate Time-series Forecasting
• Authors: Tajamul Ashraf, Janibul Bashir,
• Abstract summary: Climate 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.<n> Accurate forecasting is critical for monitoring these phenomena and supporting mitigation strategies.<n>Recent data-driven models for time-series forecasting, including CNNs, RNNs, and attention-based transformers, have shown promise but struggle with dependencies and limited parallelization.<n>In this work, we present Modulated Attention Focal (FATE) for reliable time-series forecasting.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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, especially in long-horizon, multivariate meteorological datasets. In this work, we present Focal Modulated Attention Encoder (FATE), a novel transformer architecture designed for reliable multivariate time-series forecasting. Unlike conventional models, FATE introduces a tensorized focal modulation mechanism that explicitly captures spatiotemporal correlations in time-series data. We further propose two modulation scores that offer interpretability by highlighting critical environmental features influencing predictions. We benchmark FATE across seven diverse real-world datasets including ETTh1, ETTm2, Traffic, Weather5k, USA-Canada, Europe, and LargeST datasets, and show that it consistently outperforms all state-of-the-art methods, including temperature datasets. Our ablation studies also demonstrate that FATE generalizes well to broader multivariate time-series forecasting tasks. For reproducible research, code is released at https://github.com/Tajamul21/FATE.

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