CASA：基于CNN自编码器得分注意力的高效多元长期时间序列预测
CASA: CNN Autoencoder-based Score Attention for Efficient Multivariate Long-term Time-series Forecasting
AI总结：
本文提出CASA机制，通过CNN自编码器得分注意力降低内存占用，以模型无关方式改进Transformer，在多元长期时间序列预测中最多降低77.7%计算资源并取得最优性能。
摘要：
多元长期时间序列预测对于天气预报和交通分析等应用至关重要。此外，Transformer变体的实现提高了预测精度。继这些变体之后，不同的输入数据处理方法也推动了该领域的发展，例如逐点、逐通道和逐块标记化等标记化技术。然而，以往研究在时间复杂度、计算资源和跨维度交互方面仍存在局限性。为解决这些局限性，我们提出了一种新颖的基于CNN自编码器的得分注意力机制（CASA），该机制能够以模型无关的方式引入多种Transformer模型，通过降低内存占用并提升模型性能。在八个真实世界数据集上的实验验证表明，CASA将计算资源最多降低77.7%，推理速度提升44.0%，并取得了最先进的性能，在87.5%的评估指标中排名第一。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
