  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.
