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Yong Liu

15 papers hereh-index 103.2k citations19 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author6
  • middle author6
  • last author2

Across the 14 of 15 papers where every author was matched, so the position is known.

fields
  • cs.LG13
  • cs.AI2
same name
  • Yong Liu — 49 papers, h 75
  • Yong Liu — 40 papers, h 16
  • Yong Liu — 29 papers, h 22
  • Yong Liu — 28 papers, h 14
  • Yong Liu — 24 papers, h 13
  • Yong Liu — 23 papers, h 12

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20232026
most citediTransformer: Inverted Transformers Are Effective for Time Series Forecasting

383 citations · 469 across the 14 of their papers we have counts for

collaborators
Showing 2025Show all

4 papers · 1 filter

cs.LG2025

CoRA: Covariate-Aware Adaptation of Time Series Foundation Models

Guo Qin, Zhi Chen, Yong Liu +5

Time Series Foundation Models (TSFMs) have shown significant impact through their model capacity, scalability, and zero-shot generalization. However, due to the heterogeneity of in…

cs.LG2025

Exploring Accuracy Law for Deep Time Series Forecasters: An Empirical Study

Yuxuan Wang, Haixu Wu, Yuezhou Ma +8

Deep time series forecasting has emerged as a rapidly growing field in recent years. Despite the exponential growth of community interests, progress on standard benchmarks is often…

cs.LG2025

TimesBERT: A BERT-Style Foundation Model for Time Series Understanding

Haoran Zhang, Yong Liu, Yunzhong Qiu +4

Time series analysis is crucial in diverse scenarios. Beyond forecasting, considerable real-world tasks are categorized into classification, imputation, and anomaly detection, unde…

cs.LG2025★ 2 cited

Sundial: A Family of Highly Capable Time Series Foundation Models

Yong Liu, Guo Qin, Zhiyuan Shi +5

We introduce Sundial, a family of native, flexible, and scalable time series foundation models. To predict the next-patch's distribution, we propose a TimeFlow Loss based on flow-m…

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