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Feng Li News

Feng Li is interviewed by the Forecasting Impact Podcast

 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 […]

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Feng Li News Yanfei Kang

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

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 […]

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

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 […]

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Feng Li News Yanfei Kang

New Paper: Bayesian forecast combination using time-varying features

Authors: Li Li, Yanfei Kang, Feng Li Abstract: In this work, we propose a novel framework for density forecast combination by constructing time-varying weights based on time series features, which is called Feature-based Bayesian Forecasting Model Averaging (FEBAMA). Our framework estimates weights in the forecast combination via Bayesian log predictive scores, in which the optimal […]

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Feng Li News Yanfei Kang

The FFORMPP paper is accepted in the International Journal of Forecasting

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 […]

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Feng Li News Yanfei Kang

Meet our KLLAB members on ISF 2021

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 […]

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Feng Li News

Feng Li will be presenting at the ISBA 2021 meeting

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 […]

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

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 […]

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Feng Li News

New Paper: Forecasting reconciliation with a top-down alignment of independent level forecasts

Authors: Matthias Anderer and Feng Li Abstract: Hierarchical forecasting with intermittent time series is a challenge in both research and empirical studies. The overall forecasting performance is heavily affected by the forecasting accuracy of intermittent time series at bottom levels. In this paper, we present a forecasting reconciliation approach that treats the bottom level forecast as latent […]

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Feng Li News Yanfei Kang

The fuma paper is accepted in Journal of the Operational Research Society

Our fuma paper is accepted in the Journal of the Operational Research Society. Xiaoqian Wang, Yanfei Kang, Fotios Petropoulos, Feng Li (2021). The uncertainty estimation of feature-based forecast combinations (in press), Journal of the Operational Research Society.  [ Working paper | R package ] Forecasting is an indispensable element of operational research (OR) and an important aid to planning. The accurate estimation […]