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New DeRegiME model improves probabilistic forecasting under distribution shift

By PulseAugur Editorial · [2 sources] ·

Researchers have developed DeRegiME, a novel probabilistic forecasting method designed to handle distribution shifts in time series data. This approach uses a deep mixture of experts model with a sparse variational Gaussian process to separate latent uncertainty regimes from the underlying signal. DeRegiME offers an interpretable decomposition of mean, residual, and noise, effectively identifying changepoints and improving forecasting accuracy. AI

IMPACT Introduces a new method for more accurate and interpretable time series forecasting, particularly in scenarios with changing data distributions.

RANK_REASON Publication of a new academic paper detailing a novel machine learning model.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New DeRegiME model improves probabilistic forecasting under distribution shift

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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Kieran Wood, Stefan Zohren, Stephen J. Roberts ·

    DeRegiME: Deep Regime Mixtures for Probabilistic Forecasting under Distribution Shift

    arXiv:2605.19231v1 Announce Type: cross Abstract: We introduce DeRegiME -- Deep Regime Mixture of Experts -- a direct multi-horizon probabilistic forecaster that separates latent uncertainty regimes from the underlying signal and softly assigns each forecast location to learned r…

  2. arXiv stat.ML TIER_1 English(EN) · Stephen J. Roberts ·

    DeRegiME: Deep Regime Mixtures for Probabilistic Forecasting under Distribution Shift

    We introduce DeRegiME -- Deep Regime Mixture of Experts -- a direct multi-horizon probabilistic forecaster that separates latent uncertainty regimes from the underlying signal and softly assigns each forecast location to learned recurring regimes using a sparse variational Gaussi…

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