提出DGF框架，让时间序列预测保留多模式动态变化。
Dirichlet-Guided Group Forecasting for Alleviating Over-smoothing in Time Series Forecasting
- 用狄利克雷引导分层采样，显式建模多种未来模式
- 在真实数据集上提升准确率、多样性和动态一致性
- 适合需要捕捉复杂波动和转折点的预测场景
时间序列预测常因过平滑而失效，尤其在多模态未来动态下：预测可能跟随观测趋势，却丢失尖锐变化、振荡、拐点和状态转换等关键动态特征。本文从潜在动态模式压缩视角重新审视该问题——在部分观测与单次实现监督下，多个合理未来模式可能被弱化、合并或平均。为此提出狄利克雷引导的组预测（DGF）框架，显式建模多种模式条件下的预测分布及模式选择概率的不确定性。DGF采用狄利克雷引导的分层采样机制与基于奖励的优化策略，鼓励预测具备高精度、动态一致性和模式区分性。在多个真实世界基准上的实验表明，DGF有效缓解过平滑，同时提升预测准确性、多样性与动态一致性。
原文摘要 · Abstract (English)
Time series forecasting often suffers from over-smoothing, especially when future dynamics are multi-modal. Forecasts may follow the coarse trend of the observed future, but fail to preserve sharp changes, oscillations, turning points, and regime transitions that define plausible dynamic evolution. In this work, we revisit over-smoothing from the perspective of latent dynamical mode compression: under partial observation and single-realization supervision, multiple plausible future modes can be weakened, merged, or averaged during forecasting. Based on this view, we propose Dirichlet-Guided Group Forecasting (DGF), a mode-preserving forecasting framework that explicitly models multiple mode-conditioned predictive distributions and uncertainty over their selection probabilities. DGF uses a Dirichlet-guided hierarchical sampling mechanism and reward-based optimization to encourage forecasts that are accurate, dynamically consistent, and mode-distinct. Extensive experiments on real-world forecasting benchmarks show that DGF reduces over-smoothing while improving forecasting accuracy, diversity, and dynamical consistency.
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