← Back to all news
arXiv AI By Hao Wang, Licheng Pan, Yuan Lu, Zhixuan Chu, Xiaoxi Li, Shuting He, Zhichao Chen, Haoxuan Li, Qingsong Wen, Zhouchen Lin

DistDF: Time-Series Forecasting Needs Joint-Distribution Wasserstein Alignment

Read the original on arXiv AI →

The Flow has not summarised this story yet — read it at arXiv AI.

Related stories

arXiv Statistics ML
Aug 24

Wasserstein Exponential Smoothing for Distributional Time Series Forecasting

The paper introduces Wasserstein Exponential Smoothing (WES), a single‑parameter recursive method for forecasting distributional time series on ℝ. WES updates forecast distributions along Wasserstein geodesics, allowing direct application to empirical distributions without parametric modeling. In high‑frequency equity‑index return and household electricity‑demand data, WES achieves the lowest one‑step‑ahead Wasserstein prediction error among existing benchmarks and is retained in the 90% model confidence set for all 20 series examined.

By Takuo Matsubara, Peiwen Jiang, Minh-Ngoc Tran, Wilson Ye Chen
benchmarks
More like this →
arXiv Machine Learning
Oct 12

Realism VS Accuracy: Event Sequence Forecasting from a Generative Modeling Perspective

The paper investigates multi‑horizon forecasting of event sequences as a conditional generative modeling problem, evaluating forecasts for both prediction accuracy and distributional realism. It introduces a benchmark with metrics, baselines, and real‑world datasets, revealing a trade‑off between accuracy and realism. The authors propose Wasserstein BaryBooster, an algorithm that combines samples from a realism‑strong diffusion model with a state‑of‑the‑art predictor to convert distributional realism into improved prediction accuracy.

By Dmitry Osin, Egor Surkov, Petr Mokrov, Igor Udovichenko, Ivan Karpukhin, Evgeny Burnaev, Egor Shvetsov, Alexander Korotin
diffusionbenchmarks
More like this →
arXiv AI
Oct 9

AdaCast: Conditional Parameter Generation for Adaptive Time Series Forecasting

AdaCast introduces a conditional parameter generation framework that tailors time‑series forecasting models to each input by producing low‑rank updates for a frozen pretrained foundation model. Unlike static adaptation methods that apply a single set of updates to all inputs, AdaCast generates input‑specific updates during both training and inference. Experiments on six public benchmarks show that AdaCast consistently outperforms static baselines in in‑domain forecasting and improves zero‑shot generalization to unseen datasets across domains.

By Darahaas Nallagatla, Darryl Cherian Jacob, Pan He
benchmarks
More like this →
arXiv Machine Learning
Oct 7

Deep Time-Series Forecasting in 10 Years: A Survey

arXiv:2603.19899v2 Announce Type: replace-cross Abstract: Autocorrelation is a common property of time-series, where each observation is dependent on its predecessors. In deep time-series forecasting...

By Hao Wang, Licheng Pan, Qingsong Wen, Jialin Yu, Zhichao Chen, Chunyuan Zheng, Xiaoxi Li, Zhixuan Chu, Chao Xu, Mingming Gong, Haoxuan Li, Yuan Lu, Zhouchen Lin, Philip Torr, Yan Liu
More like this →