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Feng Li News Yanfei Kang

The darima paper is published in the International Journal of Forecasting

Authors:  Xiaoqian Wang, Yanfei Kang, Rob J Hyndman and Feng Li Providing forecasts for ultra-long time series plays a vital role in various activities, such as investment decisions, industrial production arrangements, and farm management. This paper develops a novel distributed forecasting framework to tackle challenges associated with forecasting ultra-long time series by utilizing the industry-standard MapReduce framework. The proposed model […]

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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 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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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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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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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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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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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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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 […]