As time evolves, data within specific domains exhibit predictability that
motivates time series forecasting to predict future trends from historical
data. However, current deep forecasting methods can achieve promising
performance but generally lack interpretability, hindering trustworthiness and
practical deployment in safety-critical applications such as auto-driving and
healthcare. In this paper, we propose a novel interpretable model, iTFKAN, for
credible time series forecasting. iTFKAN enables further exploration of model
decision rationales and underlying data patterns due to its interpretability
achieved through model symbolization. Besides, iTFKAN develops two strategies,
prior knowledge injection, and time-frequency synergy learning, to effectively
guide model learning under complex intertwined time series data. Extensive
experimental results demonstrated that iTFKAN can achieve promising forecasting
performance while simultaneously possessing high interpretive capabilities.

Este artículo explora los viajes en el tiempo y sus implicaciones.

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