用不确定性动态加权专家，提升时间序列预测精度与可信度。
MoGU: Mixture-of-Gaussians with Uncertainty-based Gating for Time Series Forecasting
- 以预测方差作为门控信号，自动调节各专家贡献。
- 在多基准测试中均优于传统MoE，且预测区间更紧凑。
- 适合需要高可靠性不确定性的时序预测场景。
我们提出一种面向回归任务的新型混合专家框架MoGU（基于不确定性门控的高斯混合模型）。该方法摒弃传统学习型门控，改用专家自身预测不确定性作为原生路由信号。通过将每个预测建模为高斯分布，系统利用预测方差动态调整各专家权重。我们在多变量时间序列预测任务上验证了MoGU的性能，该领域具有高波动性和变化的噪声模式。在多个基准、预测跨度和骨干网络上的实证结果表明，与传统MoE相比，MoGU在预测精度上持续提升。进一步通过置信区间校准评估显示，我们的方法生成的预测区间比现有基线更高效。这些发现表明，MoGU兼具卓越性能与可靠的高保真不确定性量化能力。代码已开源：https://github.com/yolish/moe_unc_tsf。
原文摘要 · Abstract (English)
We introduce Mixture-of-Gaussians with Uncertainty-based Gating (MoGU), a novel Mixture-of-Experts (MoE) framework designed for regression tasks. MoGU replaces standard learned gating with an intrinsic routing paradigm where expert-specific uncertainty serves as the native gating signal. By modeling each prediction as a Gaussian distribution, the system utilizes predicted variance to dynamically weight expert contributions. We validate MoGU on multivariate time-series forecasting, a domain defined by high volatility and varying noise patterns. Empirical results across multiple benchmarks, horizon lengths, and backbones demonstrate that MoGU consistently improves forecasting accuracy compared to traditional MoE. Further evaluation via conformal prediction indicates that our approach yields more efficient prediction intervals than existing baselines. These findings highlight MoGU's capacity for providing both competitive performance and reliable, high-fidelity uncertainty quantification. Our code is available at: https://github.com/yolish/moe_unc_tsf
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