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Paper accepted in IJF: Exploring the social influence of Kaggle virtual community on the M5 competition

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 platform of data scientists and machine learning practitioners. Kaggle provides a gathering place, or virtual community, for web users who are interested in the M5 competition. Users […]

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Paper accepted in IJF: Exploring the representativeness of the M5 competition data

Authors: Evangelos Theodorou#, Shengjie Wang#, Yanfei Kang*, Evangelos Spiliotis, Spyros Makridakis, Vassilios Assimakopoulos Abstract: The main objective of the M5 competition, which focused on forecasting the hierarchical unit sales of Walmart, was to evaluate the accuracy and uncertainty of forecasting methods in the field in order to identify best practices and highlight their practical implications. However, whether […]

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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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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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New Paper: Exploring the social influence of Kaggle virtual community on the M5 competition

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 […]

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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 […]

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New Paper: Forecast with Forecasts: Diversity Matters

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 […]

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We are presenting at ISF2020 Invited Session

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) […]

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The dejavu paper is accepted in the Journal of Business Research

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 […]

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The forecasting with time series imaging paper is accepted in Expert Systems with Applications

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 […]