DGF explicitly models multiple mode-conditioned predictive distributions via Dirichlet-guided sampling and reward optimization to preserve dynamical features in time series forecasts.
Proceedings of the 32nd ACM International Conference on Information and Knowledge Management , pages =
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cs.LG 3years
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UNVERDICTED 3representative citing papers
Mixture-of-experts fusing multiple pretrained forecasters achieves strongest performance on influenza time series, with pretraining gains largest at longer horizons when domain-aligned and LLM methods underperforming.
A scalable framework harmonizes spatial and temporal representations via low-rank spatial compression and extended temporal horizons to reduce prediction uncertainty in large-scale spatiotemporal tasks.
citing papers explorer
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Dirichlet-Guided Group Forecasting for Alleviating Over-smoothing in Time Series Forecasting
DGF explicitly models multiple mode-conditioned predictive distributions via Dirichlet-guided sampling and reward optimization to preserve dynamical features in time series forecasts.
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Understanding Key Features of Time Series Foundation Models from Epidemic Forecasting
Mixture-of-experts fusing multiple pretrained forecasters achieves strongest performance on influenza time series, with pretraining gains largest at longer horizons when domain-aligned and LLM methods underperforming.
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Dimensional Balance Improves Large Scale Spatiotemporal Prediction Performance
A scalable framework harmonizes spatial and temporal representations via low-rank spatial compression and extended temporal horizons to reduce prediction uncertainty in large-scale spatiotemporal tasks.