Fuzzy-MoE：面向非平稳多元时间序列预测的可解释状态条件型专家路由机制
Fuzzy-MoE: Interpretable Regime-Conditioned Expert Routing for Non-Stationary Multivariate Time Series Forecasting
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中文总结 AI 辅助
本文提出Fuzzy-MoE模型，通过双视图模糊路由器实现可解释专家路由，在非平稳多元时间序列预测中显著优于主流方法，同时具备透明可追溯的机制。
中文摘要 AI 辅助
在非平稳多元时间序列中，不同变量和样本往往呈现异质性的潜在动态状态，而现有深度预测模型通常将其压缩为统一的端到端映射，导致对时变动态的建模效果欠佳，且难以解释不同潜在状态下激活的预测机制。为克服这些局限，本文将时间序列预测重新表述为潜在时间状态识别与可解释专家路由的统一框架，并提出Fuzzy-MoE——一种基于模糊逻辑的动态混合专家（Mixture-of-Experts, MoE）模型。Fuzzy-MoE由多个并行的专家映射网络和双视图模糊路由器构成；路由器通过联合利用局部卷积动态与全局分段统计，推断潜在时间状态，并通过可学习的高斯隶属函数计算专家激活强度，实现基于显式IF-THEN规则的专家选择。这种细粒度路由策略允许同一序列内的不同变量激活不同专家，有效捕捉异质性时间动态，同时提升模型可解释性。在多个公开时间序列基准数据集上的实验结果表明，Fuzzy-MoE在预测准确率上显著优于主流预测方法；此外，模糊隶属度与规则激活提供了可解释的路由诊断结果，证明该框架在预测性能与机制透明度两方面均有效。与使用黑箱路由的传统MoE模型不同，Fuzzy-MoE的路由基于清晰、可解释的模糊规则，使专家选择过程透明且可追溯。
英文摘要
In non-stationary multivariate time series, different variables and samples often exhibit heterogeneous latent dynamic states, while existing deep forecasting models usually compress them into a unified end-to-end mapping, leading to suboptimal modeling of time-varying dynamics and limited interpretability regarding which forecasting mechanism is activated under different latent states. To overcome these limitations, we reformulate time series forecasting as a unified framework of latent temporal state identification and interpretable expert routing, and propose Fuzzy-MoE, a fuzzy logic-based dynamic Mixture-of-Experts model. Fuzzy-MoE consists of multiple parallel expert mapping networks and a dual-view fuzzy router. By jointly exploiting local convolutional dynamics and global segmented statistics, the router infers latent temporal states and computes expert activation strengths through learnable Gaussian membership functions, enabling explicit IF-THEN rule-based expert selection. This fine-grained routing strategy allows different variables within the same sequence to activate different experts, effectively capturing heterogeneous temporal dynamics while improving model interpretability. Experimental results on multiple public time series benchmark datasets show that Fuzzy-MoE significantly outperforms mainstream forecasting methods in forecasting accuracy. Moreover, fuzzy memberships and rule activations provide interpretable routing diagnostics, demonstrating the effectiveness of the proposed framework in both forecasting performance and mechanism transparency. Unlike traditional MoE models that use black-box routing, Fuzzy-MoE`s routing is based on clear, interpretable fuzzy rules. This makes the expert selection transparent and traceable.
