Category: News
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Paper accepted in IJF: Hierarchical forecasting 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. 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…
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Paper “Improving forecasting with sub-seasonal time series” accepted in IJPR
Authors: Xixi Li, Fotios Petropoulos, Yanfei Kang Abstract: Time series forecasting plays an increasingly important role in modern business decisions. In today’s data-rich environment, people often aim to choose the optimal forecasting model for their data. However, identifying the optimal model often requires professional knowledge and experience, making accurate forecasting a challenging task. To mitigate the importance…
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Keynote talk for ICDM 2021 workshop by Yanfei Kang
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…
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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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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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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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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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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…