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 […]
Category: News
Authors: Xixi Li, Yun Bai, Yanfei Kang Abstract: One of the most significant differences of M5 over previous forecasting competitions is that it was held on Kaggle, an online community of data scientists and machine learning practitioners. On the Kaggle platform, people can form virtual communities such as online notebooks and discussions to discuss their models, choice […]
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 […]
Our GRATIS paper for GeneRAting TIme Series with diverse and controllable characteristics is accepted in the ASA data science journal: Statistical Analysis and Data Mining. Yanfei Kang, Rob J Hyndman, and Feng Li*. (2020). GRATIS: GeneRAting TIme Series with diverse and controllable characteristics, Statistical Analysis and Data Mining. (In Press) [Journal version | Working Paper | R Package | Web App] The explosion […]
The DeepTCN paper is accepted in Neurocomputing. Yitian Chen, Yanfei Kang, Yixiong Chen and Zizhuo Wang. (2020). Probabilistic Forecasting with Temporal Convolutional Neural Network. Neurocomputing 399C (2020) pp. 491-501. [ Online | Working Paper | Software] We present a probabilistic forecasting framework based on convolutional neural network (CNN) for multiple related time series forecasting. The framework can be […]
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We have a lab now!
We are so excited to announce that Yanfei and Feng’s group now has an official lab, named KLLAB (pronounced as [col·lab], meaning collaborating) and a website KLLAB.org. Have fun!