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.

我们提出了 Temporal Kolmogorov-Arnold Transformer (TKAT) 模型，这是一种新颖的基于注意力机制的架构，旨在通过时间 Kolmogorov-Arnold 网络来捕捉多元数据流中的复杂时间模式和关系，以实现对时间序列中的长期依赖的建模。