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Knowledge Leads to Limitless Aspirations and Boundaries

Author: Feng Li

  • The darima paper is published in the International Journal of Forecasting

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    Feng Li
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    Authors:  Xiaoqian Wang, Yanfei Kang, Rob J Hyndman and Feng Li Providing forecasts for ultra-long time series plays a vital role in various activities, such as investment decisions, industrial production arrangements, and farm management. This paper develops a novel distributed forecasting framework to tackle challenges associated with forecasting ultra-long time series by utilizing the industry-standard MapReduce framework. The proposed model…

  • Paper accepted in IJF: Hierarchical forecasting with a top-down alignment of independent level forecasts

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    Feng Li
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    Authors: Matthias Anderer and Feng Li Abstract: Hierarchical forecasting with intermittent time series is a challenge in both research and empirical studies. Extensive research focuses on improving the accuracy of each hierarchy, especially the intermittent time series at bottom levels. Then hierarchical reconciliation could be used to improve the overall performance further. In this paper, we present…

  • Keynote talk for ICDM 2021 workshop by Yanfei Kang

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    Feng Li
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    Professor Yanfei Kang gave a keynote talk on “Feature-based time series forecasting” on SFE-TSDM Workshop at 21st IEEE International Conference on Data Mining (IEEE ICDM 2021). The workshop on Systematic Feature Engineering for Time-Series Data Mining is organized as part of the 21st IEEE International Conference on Data Mining, which will be held from 7-10 December 2021 in…

  • Feng Li is interviewed by the Forecasting Impact Podcast

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    Feng Li
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     In this episode, Feng Li describes the current status of forecasting science and practice in China, his research focus, and his lab KLLAB, where he and his wife Dr Yanfei Kang are focused on computing, forecasting and learning with massive machines. We also discussed in depth one of his papers entitled “Time series forecasting with…

  • The Diversity paper is accepted in the European Journal of Operational Research

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    Feng Li
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    Authors: Yanfei Kang, Wei Cao, Fotios Petropoulos & Feng Li* Abstract: Forecast combinations have been widely applied in the last few decades to improve forecasting. Estimating optimal weights that can outperform simple averages is not always an easy task. In recent years, the idea of using time series features for forecast combination has flourished. Although this idea has been proved…

  • The dqr paper is published in the Journal of Business & Economic Statistics

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    Feng Li
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    Title: A Note on Distributed Quantile Regression by Pilot Sampling and One-Step Updating Authors:  Rui Pan, Tunan Ren, Baishan Guo, Feng Li, Guodong Li, and Hansheng Wang Abstract: Quantile regression is a method of fundamental importance. How to efficiently conduct quantile regression for a large dataset on a distributed system is of great importance. We show that the…

  • The FFORMPP paper is accepted in the International Journal of Forecasting

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    Feng Li
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    The FFORMPP paper is accepted in the International Journal of Forecasting Thiyanga S. Talagala, Feng Li, Yanfei Kang (2021). FFORMPP: Feature-based forecast model performance prediction. International Journal of Forecasting. (in press) [ arXiv | R package ] This paper introduces a novel meta-learning algorithm for time series forecast model performance prediction. We model the forecast error as a function of time series features…

  • Meet our KLLAB members on ISF 2021

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    Feng Li
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    Our KLLAB members will be presenting our work at the invited session of the 41st International Symposium on Forecasting virtually. ECR – Visibility Panel Time: Mon. Jun 28, 2021 11:00 AM – 12:00 PM (UTC+8) https://whova.com/portal/webapp/iiofe_202106/Agenda/1753058 Chaired by Shari De Baets In a world ruled by the internet and social media, it is more important than ever…

  • Feng Li will be presenting at the ISBA 2021 meeting

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    Feng Li
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    Dr. Feng Li will be presenting at the 2021 world meeting of the International Society for Bayesian Analysis (ISBA) on session of C28: Bayesian Inference and Forecasting for Business Problems. Time: Friday, July 2, 6:45 am – 8:00 am Whova Link: https://whova.com/embedded/session/isbaw_202106/1572928/ Meeting Program: https://events.stat.uconn.edu/ISBA2021/programs.html The International Society for Bayesian Analysis (ISBA) was founded in 1992to promote the development and application of Bayesian analysis. By sponsoring…

  • The dlsa paper is accepted in the Journal of Computational and Graphical Statistics

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    Feng Li
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    Authors:  Xuening Zhu, Feng Li and Hansheng Wang Abstract:  In this work we develop a distributed least squares approximation (DLSA) method, which is able to solve a large family of regression problems (e.g., linear regression, logistic regression, Cox’s model) on a distributed system. By approximating the local objective function using a local quadratic form, we are able to obtain a…