Dirichlet-Guided Group Forecasting for Alleviating Over-smoothing in Time Series Forecasting

2026-06-09

AI Summary

This work addresses the challenge of over-smoothing in time series forecasting, where multimodal future dynamics are often lost during compression, leading to difficulties in capturing sharp transitions, oscillations, and state changes. The study introduces a mode-preserving forecasting framework that explicitly models multimodal predictive distributions along with their selection probabilities. By incorporating Dirichlet-guided hierarchical sampling and a reward-driven optimization mechanism, the proposed approach effectively mitigates over-smoothing while preserving dynamic diversity. Experimental results demonstrate that the method significantly improves prediction accuracy, diversity, and dynamic consistency across multiple real-world benchmarks.

Abstract

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.

Problem

• over-smoothing
• time series forecasting
• multi-modal dynamics
• mode preservation
• dynamical consistency

Innovation

• over-smoothing
• Dirichlet-guided forecasting
• multi-modal dynamics
• mode-preserving
• time series forecasting