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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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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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 […]
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 […]
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 […]
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 […]
Authors: Yanfei Kang, Wei Cao, Fotios Petropoulos, Feng Li Abstract: 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 […]
Our lab members will be presenting our work at the invited session of the 40th International Symposium on Forecasting virtually. Session: Forecast Combination Time: October 26, Monday, 17:00-18:00 GMT+8 Detailed Schedule: https://whova.com/embedded/session/iiofe_202006/1323449/ Speakers Yanfei Kang (Speaker) Associate Professor, School of Economics and Management, Beihang University Xiaoqian Wang (Speaker) PhD student, Beihang University Xixi Li (Speaker) […]
Our Dejavu paper is accepted in the Journal of Business Research. Yanfei Kang, Evangelos Spiliotis, Fotios Petropoulos, Nikolaos Athiniotis, Feng Li, Vassilios Assimakopoulo (2020). Déjà vu: A data-centric forecasting approach through time series cross-similarity, Journal of Business Research. (In Press) [Working Paper | Software] Accurate forecasts are vital for supporting the decisions of modern companies. Forecasters typically […]
Our foresting paper with time series imaging approach is accepted in Expert Systems with Applications. Xixi Li, Yanfei Kang, and Feng Li*. (2020). Forecasting with time series imaging, Expert Systems with Applications. (In Press) [Working Paper | Software] Feature-based time series representations have attracted substantial attention in a wide range of time series analysis methods. Recently, the use of […]