MoGU: Mixture-of-Gaussians with Uncertainty-based Gating for Time Series Forecasting

• URL: http://arxiv.org/abs/2510.07459v1
• Date: Wed, 08 Oct 2025 19:04:25 GMT
• Title: MoGU: Mixture-of-Gaussians with Uncertainty-based Gating for Time Series Forecasting
• Authors: Yoli Shavit, Jacob Goldberger,
• Abstract summary: We introduce Mixture-of-Gaussians with Uncertainty-based Gating (MoGU)<n>Unlike conventional MoEs that provide only point estimates, MoGU models each expert's output as a Gaussian distribution.<n>MoGU's core innovation is its uncertainty-based gating mechanism, which replaces the traditional input-based gating network.
• Score: 14.843307803219611
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce Mixture-of-Gaussians with Uncertainty-based Gating (MoGU), a novel Mixture-of-Experts (MoE) framework designed for regression tasks and applied to time series forecasting. Unlike conventional MoEs that provide only point estimates, MoGU models each expert's output as a Gaussian distribution. This allows it to directly quantify both the forecast (the mean) and its inherent uncertainty (variance). MoGU's core innovation is its uncertainty-based gating mechanism, which replaces the traditional input-based gating network by using each expert's estimated variance to determine its contribution to the final prediction. Evaluated across diverse time series forecasting benchmarks, MoGU consistently outperforms single-expert models and traditional MoE setups. It also provides well-quantified, informative uncertainties that directly correlate with prediction errors, enhancing forecast reliability. Our code is available from: https://github.com/yolish/moe_unc_tsf

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