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 […]
Category: Yanfei Kang
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: 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: 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 […]
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 […]
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 […]
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 […]
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 […]