Dirichlet-Guided Group Forecasting for Alleviating Over-smoothing in Time Series Forecasting
arXiv:2606.10592v1 Announce Type: new 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. Ba
The continuous drive for more accurate and robust AI models in time series forecasting is pushing research into addressing fundamental limitations like over-smoothing.
Improved time series forecasting, especially in multi-modal scenarios, can significantly enhance predictive capabilities across finance, climate, and operational planning.
This research introduces a method to preserve critical dynamic features in forecasts, moving beyond coarse trend predictions toward more precise and actionable insights.
- · AI/ML researchers
- · Quantitative finance
- · Supply chain logistics
- · Energy grid operators
- · Forecasting models relying solely on coarse trends
- · Industries with high tolerance for over-smoothed predictions
Time series models will become more adept at identifying and predicting sharp changes and regime shifts.
This improved accuracy will lead to better resource allocation and risk management in sectors heavily reliant on future predictions.
More reliable forecasting could enable more sophisticated autonomous systems that react proactively to complex, volatile environments.
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Read at arXiv cs.LG