The Phong Le

MoE-KAN-AD: Mixture-of-Experts Kolmogorov-Arnold Autoencoders for Univariate Time Series Anomaly Detection

Univariate time series anomaly detection remains difficult because real-world signals are nonlinear, noisy, and often contaminated with outliers. To overcome the parameter explosion and noise sensitivity of standard Kolmogorov-Arnold Networks (KANs), we propose MoE-KAN-AD. Our architecture introduces a shared dictionar...