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New Paper: Forecasting reconciliation 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. The overall forecasting performance is heavily affected by the forecasting accuracy of intermittent time series at bottom levels. In this paper, we present a forecasting reconciliation approach that treats the bottom level forecast as latent […]

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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: Improving forecasting with sub-seasonal time series patterns

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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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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Feng Li News Yanfei Kang

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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New Paper: Distributed ARIMA Models for Ultra-long Time Series

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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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 […]

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Feng Li News Yanfei Kang

The GRATIS paper is accepted in Statistical Analysis and Data Mining

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 […]