Title: Forecast combinations: An over 50-year review Authors: Xiaoqian Wang, Rob J. Hyndman, Feng Li and Yanfei Kang Journal: International Journal of Forecasting Summary: The idea of combining multiple individual forecasts dates back to Francis Galton, who in 1906 visited an ox-weight-judging competition and observed that the average of 787 estimates of an ox’s weight […]
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Authors: Li Li, Yanfei Kang, Fotios Petropoulos and Feng Li Links: [ DOI | Preprint ] Summary: Intermittent demand with several periods of zero demand is ubiquitous in practice. Over half of inventory consists of spare parts, in which demand is typically intermittent (Nikolopoulos Citation2021). Given the high purchase and shortage costs associated with intermittent demand […]
Authors: Bohan Zhang, YanfeiKang, AnastasiosPanagiotelis, FengLi Journal: European Journal of Operational Research Links: [ DOI | Preprint | Python code ] Summary: In this paper, we propose a forecast reconciliation approach that can keep the base forecasts of specific levels or multiple nodes from different levels immutable after reconciliation. The proposed method is flexible and […]
Authors: Li Li, Yanfei Kang, Feng Li Summary: Achieving a robust and accurate forecast is a central focus in finance and econometrics. Forecast combination has been adopted as an essential enhancement tool for improving time series forecasting performance during recent decades (Bergmeir et al., 2016, Garratt et al., 2019, Kolassa, 2011), due to its ability to reduce the […]
Authors: Zhiru Wang, Yu Pang, Mingxin Gan, Martin Skitmore, Feng Li Link: https://doi.org/10.1016/j.ssci.2022.105850 Summary: The semi-open character with high passenger flow in Metro Rail Transport Stations (MRTS) makes safety management of human-electromechanical interaction escalator systems more complex. Safety management should not consider only single failures, but also the complex interactions in the system. This study […]
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 […]
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 […]
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 […]
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 […]
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 […]