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Probabilistic Forecasting

5 articles · Page 1 of 1
Machine Heart
Machine Heart
Sep 25, 2026 · Artificial Intelligence

TimePrism: Probabilistic Parallel Generation Predates Jev by a Year

CUHK's TimePrism paper, accepted at ICLR 2026, introduced a probabilistic scenarios paradigm that outputs multiple futures with explicit probabilities in a single forward pass using only three linear layers, outperforming complex models on benchmarks—a year before Jev popularized similar decision-oriented, parallel probability generation for AI agents.

CUHKDecision-Oriented AIExplicit Probabilities
0 likes · 11 min read
TimePrism: Probabilistic Parallel Generation Predates Jev by a Year
DeepHub IMBA
DeepHub IMBA
Sep 4, 2026 · Artificial Intelligence

TimesFM-3: Google's Native Multivariate Forecasting with One-Pass Decoding

Google's TimesFM-3 introduces native multivariate time series forecasting with non-autoregressive decoding, enabling joint predictions across multiple targets and covariates in a single forward pass, outperforming competitors on benchmarks while offering zero-shot generalization and probabilistic outputs.

Foundation ModelsProbabilistic ForecastingTime Series Forecasting
0 likes · 15 min read
TimesFM-3: Google's Native Multivariate Forecasting with One-Pass Decoding
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Jun 13, 2026 · Artificial Intelligence

Paper Reading: TimeGMM – An Adaptive GMM Framework for Probabilistic Time‑Series Forecasting

TimeGMM introduces an adaptive Gaussian‑mixture‑model framework with reversible instance normalization, a dual‑branch time encoder, and a conditional decoder, achieving up to 22.48 % improvement in CRPS and 21.23 % in NMAE over state‑of‑the‑art probabilistic forecasting methods across multiple benchmark datasets.

Adaptive NormalizationGaussian Mixture ModelProbabilistic Forecasting
0 likes · 15 min read
Paper Reading: TimeGMM – An Adaptive GMM Framework for Probabilistic Time‑Series Forecasting
Meituan Technology Team
Meituan Technology Team
Aug 18, 2022 · Artificial Intelligence

Highlights of Meituan’s KDD 2022 Papers

This article presents concise introductions and download links for seven Meituan research papers accepted at KDD 2022, covering knowledge‑graph pre‑training, automatic feature and architecture selection for pre‑ranking, user intent discovery, persuasion factor modeling, counterfactual policy learning for top‑K recommendation, probabilistic forecasting of food preparation time, and a multi‑stage bonus allocation framework for meal delivery.

KDD 2022Probabilistic Forecastingbonus allocation
0 likes · 15 min read
Highlights of Meituan’s KDD 2022 Papers