Yanfei Kang

Dr. Yanfei Kang

orcid

Research Focus & Metrics

Academic Career Length

15Years (2012-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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19 January 2022 in International Journal of Forecasting
Verified
Journal Article
Open access
FPFotios Petropoulos
Fotios Petropoulos
Daniele ApilettiDaniele Apiletti
Daniele Apiletti
VAVassilios Assimakopoulos
Vassilios Assimakopoulos
MBMohamed Zied Babai
Mohamed Zied Babai
DBDevon K. Barrow
Devon K. Barrow
FZFlorian Ziel
Florian Ziel

1 July 2020 in Neurocomputing
Verified
Journal Article
Closed access
YCYitian Chen
Yitian Chen
YKYanfei Kang
Yanfei Kang
YCYixiong Chen
Yixiong Chen
ZWZizhuo Wang
Zizhuo Wang

20 December 2022 in International Journal of Forecasting
Verified
Journal Article
Closed access
XWXiaoqian Wang
Xiaoqian Wang
RHRob J. Hyndman
Rob J. Hyndman
FLFeng Li
Feng Li
YKYanfei Kang
Yanfei Kang

1 April 2017 in International Journal of Forecasting
Verified
Journal Article
Closed access
YKYanfei Kang
Yanfei Kang
RHRob Hyndman
Rob Hyndman
PSProfessor Smith-Miles
Professor Smith-Miles

25 August 2023 in Forecasting
Verified
Journal Article
Open access
PDF file
Spyros MakridakisSpyros Makridakis
Spyros Makridakis
Fotios PetropoulosFotios Petropoulos
Fotios Petropoulos
YKYanfei Kang
Yanfei Kang
ChatGPT, a state-of-the-art large language model (LLM), is revolutionizing the AI field by exhibiting humanlike skills in a range of tasks that include understanding and answering natural language questions, translating languages, writing code, passing professional exams, and even composing poetry, among its other abilities. ChatGPT has gained an immense popularity since its launch, amassing 100 million active monthly users in just two months, thereby establishing itself as the fastest-growing consumer application to date. This paper discusses the reasons for its success as well as the future prospects of similar large language models (LLMs), with an emphasis on their potential impact on forecasting, a specialized and domain-specific field. This is achieved by first comparing the correctness of the answers of the standard ChatGPT and a custom one, trained using published papers from a subfield of forecasting where the answers to the questions asked are known, allowing us to determine their correctness compared to those of the two ChatGPT versions. Then, we also compare the responses of the two versions on how judgmental adjustments to the statistical/ML forecasts should be applied by firms to improve their accuracy. The paper concludes by considering the future of LLMs and their impact on all aspects of our life and work, as well as on the field of forecasting specifically. Finally, the conclusion section is generated by ChatGPT, which was provided with a condensed version of this paper and asked to write a four-paragraph conclusion.

22 June 2022 in International Journal of Forecasting
Verified
Journal Article
Closed access
XWXiaoqian Wang
Xiaoqian Wang
YKYanfei Kang
Yanfei Kang
RHRob J. Hyndman
Rob J. Hyndman
FLFeng Li
Feng Li