Proceedings

CASA: CNN Autoencoder-based Score Attention for Efficient Multivariate Long-term Time-series Forecasting

Minhyuk Lee, Hyekyung Yoon, MyungJoo Kang

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 5563-5570.

https://doi.org/10.24963/ijcai.2025/619

Keywords

  • Machine Learning: ML: Deep learning architectures
  • Machine Learning: ML: Convolutional networks
  • Machine Learning: ML: Time series and data streams

Abstract

Multivariate long-term time series forecasting is critical for applications such as weather prediction, and traffic analysis. In addition, the implementation of Transformer variants has improved prediction accuracy. Following these variants, different input data process approaches also enhanced the field, such as tokenization techniques including point-wise, channel-wise, and patch-wise tokenization. However, previous studies still have limitations in time complexity, computational resources, and cross-dimensional interactions. To address these limitations, we introduce a novel CNN Autoencoder-based Score Attention mechanism (CASA), which can be introduced in diverse Transformers model-agnosticically by reducing memory and leading to improvement in model performance. Experiments on eight real-world datasets validate that CASA decreases computational resources by up to 77.7%, accelerates inference by 44.0%, and achieves state-of-the-art performance, ranking first in 87.5% of evaluated metrics. Our code is available at https://github.com/lmh9507/CASA.

Minhyuk Lee, Hyekyung Yoon, and MyungJoo Kang. 2025. CASA: CNN Autoencoder-based Score Attention for Efficient Multivariate Long-term Time-series Forecasting. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 5563–5570. https://doi.org/10.24963/ijcai.2025/619
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