Capturing complex temporal patterns and relationships within multivariate data streams is a difficult task. We propose the Temporal Kolmogorov-Arnold Transformer (TKAT), a novel attention-based architecture designed to address this task using Temporal Kolmogorov-Arnold Networks (TKANs). Inspired by the Temporal Fusion Transformer (TFT), TKAT emerges as a powerful encoder-decoder model tailored to handle tasks in which the observed part of the features is more important than the a priori known part. This new architecture combined the theoretical foundation of the Kolmogorov-Arnold representation with the power of transformers. TKAT aims to simplify the complex dependencies inherent in time series, making them more "interpretable". The use of transformer architecture in this framework allows us to capture long-range dependencies through self-attention mechanisms.

翻译：从多元数据流中捕捉复杂的时序模式与关联是一项困难的任务。我们提出了时序Kolmogorov-Arnold Transformer（TKAT），这是一种基于注意力机制的新型架构，旨在使用时序Kolmogorov-Arnold网络（TKANs）来解决该任务。受时序融合Transformer（TFT）的启发，TKAT发展为一个强大的编码器-解码器模型，专门适用于特征中观测部分比先验已知部分更重要的任务。该新架构将Kolmogorov-Arnold表示的理论基础与Transformer的强大能力相结合。TKAT旨在简化时间序列中固有的复杂依赖关系，使其更具“可解释性”。在此框架中使用Transformer架构使我们能够通过自注意力机制捕捉长程依赖关系。