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arXiv AI By Lan Guo, Jie Xiao, Zhao Su, Jun Shen, Haoran Li, Weixia Ma, Qingguo Zhou, Binbin Yong

Fuzzy-MoE: Interpretable Regime-Conditioned Expert Routing for Non-Stationary Multivariate Time Series Forecasting

Read the original on arXiv AI →

The paper introduces Fuzzy-MoE, a fuzzy logic‑based Mixture‑of‑Experts model that jointly identifies latent temporal states and routes forecasts to appropriate experts in non‑stationary multivariate time series. By combining local convolutional dynamics with global segmented statistics, the dual‑view fuzzy router uses learnable Gaussian membership functions to compute expert activation strengths, enabling explicit IF‑THEN rule‑based expert selection. Experiments on several public benchmarks show that Fuzzy‑MoE outperforms mainstream forecasting methods while providing interpretable routing diagnostics.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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