---
title: "Attention as Selection: Semantic-Guided Time Series Forecasting"
authors:
  - "Xueyu Luo"
  - "Qiang Lu"
  - "Sangui Jian"
  - "Yangxue Hu"
  - "Zhengyu Ying"
  - "Ye Yu"
  - "Wenxing Lu"
  - "Yan Qiao"
doi: "10.24963/ijcai.2026/512"
url: "https://ijcai.org/proceedings/2026/512"
pdf: "https://ijcai.org/proceedings/2026/0512.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "4599-4607"
track: "Main Track"
keywords:
  - "Machine Learning: Multi-modal learning"
  - "Machine Learning: Time series and data streams"
---

# Attention as Selection: Semantic-Guided Time Series Forecasting

Xueyu Luo, Qiang Lu, Sangui Jian, Yangxue Hu, Zhengyu Ying, Ye Yu, Wenxing Lu, Yan Qiao

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 4599-4607. DOI: [10.24963/ijcai.2026/512](https://doi.org/10.24963/ijcai.2026/512)

## Abstract

Recent advances in time series forecasting (TSF) leverage large language models (LLMs) to provide semantic priors, enabling more robust forecasting under limited training data. However, directly fusing semantic signals into temporal features often induces modality entanglement and obscures local temporal structures when cross-modal correlations are weak, noisy, or inconsistent. To address these issues, we propose Attention as Selection (AAS), a dual-branch framework that decouples temporal and semantic representations. Specifically, we define cross-modal attention as a selection process, where semantic prompts are employed to induce a sparse temporal attribution distribution over temporal positions. This guides the model to focus on critical time steps without interfering with the construction of temporal representations. Furthermore, low-entropy regularization is employed alongside global cross-modal consistency constraints to regulate the selection behavior, ensuring that semantic guidance remains sparse, stable, and aligned with temporal dynamics. Extensive experiments on six real-world datasets demonstrate that AAS outperforms existing methods across various forecasting scenarios. Code is available at https://github.com/VIMLab-hfut/Attention-as-Selection.