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 […]
Author: Yanfei Kang
Dr. Yanfei Kang is Associate Professor of Statistics at Beihang University in China. Prior to that, she was Senior R&D Engineer in Big Data Group of Baidu Inc. Yanfei obtained her Ph.D. degree at Monash University in 2014. She worked as a postdoctoral research fellow during 2014 and 2015 at Monash University. Her research interests include time series forecasting, time series visualization, text mining and statistical computing.
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 […]
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 […]
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 […]
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 […]
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 […]
The DeepTCN paper is accepted in Neurocomputing. Yitian Chen, Yanfei Kang, Yixiong Chen and Zizhuo Wang. (2020). Probabilistic Forecasting with Temporal Convolutional Neural Network. Neurocomputing 399C (2020) pp. 491-501. [ Online | Working Paper | Software] We present a probabilistic forecasting framework based on convolutional neural network (CNN) for multiple related time series forecasting. The framework can be […]