  Recurrent Neural Networks (RNNs) have revolutionized many areas of machine
learning, particularly in natural language and data sequence processing. Long
Short-Term Memory (LSTM) has demonstrated its ability to capture long-term
dependencies in sequential data. Inspired by the Kolmogorov-Arnold Networks
(KANs) a promising alternatives to Multi-Layer Perceptrons (MLPs), we proposed
a new neural networks architecture inspired by KAN and the LSTM, the Temporal
Kolomogorov-Arnold Networks (TKANs). TKANs combined the strenght of both
networks, it is composed of Recurring Kolmogorov-Arnold Networks (RKANs) Layers
embedding memory management. This innovation enables us to perform multi-step
time series forecasting with enhanced accuracy and efficiency. By addressing
the limitations of traditional models in handling complex sequential patterns,
the TKAN architecture offers significant potential for advancements in fields
requiring more than one step ahead forecasting.
