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researcher

Feng Li

Peking University

21 papers hereh-index 151.6k citations32 works total

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

author position
  • first author3
  • middle author7
  • last author9

Across the 19 of 21 papers where every author was matched, so the position is known.

fields
  • stat.AP6
  • stat.ME6
  • stat.ML3
  • cs.LG2
  • stat.CO2
  • cs.AI1
affiliations
  • Peking University
Homepage
same name
  • Feng Li — 31 papers, h 26
  • Feng Li — 24 papers, h 10
  • Feng Li — 19 papers, h 13
  • Feng Li — 16 papers, h 13
  • Feng Li — 12 papers, h 6
  • Feng Li — 10 papers, h 3

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
20112026
most citedGRATIS: GeneRAting TIme Series with diverse and controllable characteristics

99 citations · 388 across the 21 of their papers we have counts for

collaborators
Showing 2023Show all

4 papers · 1 filter

cs.LG2023

Infinite forecast combinations based on Dirichlet process

Yinuo Ren, Feng Li, Yanfei Kang +1

Forecast combination integrates information from various sources by consolidating multiple forecast results from the target time series. Instead of the need to select a single opti…

cs.AI2023

Probabilistic Forecast Reconciliation with Kullback-Leibler Divergence Regularization

Guanyu Zhang, Feng Li, Yanfei Kang

As the popularity of hierarchical point forecast reconciliation methods increases, there is a growing interest in probabilistic forecast reconciliation. Many studies have utilized…

cs.LG2023

Forecasting large collections of time series: feature-based methods

Li Li, Feng Li, Yanfei Kang

In economics and many other forecasting domains, the real world problems are too complex for a single model that assumes a specific data generation process. The forecasting perform…

stat.ME2023

Discrete forecast reconciliation

Bohan Zhang, Anastasios Panagiotelis, Yanfei Kang

This paper presents a formal framework and proposes algorithms to extend forecast reconciliation to discrete-valued data to extend forecast reconciliation to discrete-valued data,…

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