Title: Pearl Judea, Causality: Models, Reasoning, and Inference, Second Edition (2009) Authors: Feng Li Journal: International Journal of Forecasting Summary: With the big popularity and success of Judea Pearl’s original causality book, this review covers the main topics updated in the second edition in 2009 and illustrates an easy-to-follow causal inference strategy in a forecast scenario. It further discusses some potential benefits […]
Authors: Spyros Makridakis, Fotios Petropoulos, Yanfei Kang* Journal: Forecasting DOI Summary ChatGPT, a state-of-the-art large language model (LLM), is revolutionizing the AI field by exhibiting humanlike skills in a range of tasks that include understanding and answering natural language questions, translating languages, writing code, passing professional exams, and even composing poetry, among its other abilities. ChatGPT […]
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 […]
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 […]