Yanfei Kang

Associate Professor, School of Economics and Management, Beihang University

Dr. Yanfei Kang is Associate Professor of Statistics at Beihang University in China. Prior to that, she was Senior R&D Engineer in Big Data Group of Baidu Inc. Yanfei obtained her Ph.D. degree at Monash University in 2014. She worked as a postdoctoral research fellow during 2014 and 2015 at Monash University. Her research interests include time series forecasting, time series features, statistical modeling and statistical computing.

Yanfei is speaking at

Invited Session
October 26, 2020
9:00 am - 10:20 am

Speakers

  • Yanfei Kang (Speaker) Associate Professor, School of Economics and Management, Beihang University
  • Xiaoqian Wang (Speaker) Phd student, Beihang University
  • Feng Li (Speaker) Associate Professor, Central University of Finance and Economics

Description

Chair: Yanfei Kang

October 26, 2020
9:00 am - 9:20 am

Speaker

  • Yanfei Kang (Speaker) Associate Professor, School of Economics and Management, Beihang University

Description

Authors: Yanfei Kang; Wei Cao; Feng Li; Fotios Petropoulos

Forecast combination has been widely applied in the last few decades to improve forecast accuracy. In recent years, the idea of using time series features to construct forecast combination model has flourished in the forecasting area. Although this idea has been proved to be beneficial in several forecast competitions such as the M3 and M4 competitions, it may not be practical in many situations. For example, the task of selecting appropriate features to build forecasting models can be a big challenge for many researchers, and the interpretation may also be obscure so that it is hard to get valuable information from them. Hence, it is crucially important to improve the interpretability of forecast combination, making it feasible in practical applications. In this work, we treat the diversity of a pool of algorithms as an alternative to state-of-the-art time series features, and use meta-learning to construct diversity-based forecast combination models. A rich set of time series are used to evaluate the performance of the proposed method. Experimental results show that our diversity-based combination forecasting framework not only simplifies the modeling process but also achieves superior forecasting performance.

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