1 citations · 1 across the 2 of their papers we have counts for
10 papers
Optimal reconciliation with immutable forecasts
Bohan Zhang, Yanfei Kang, Anastasios Panagiotelis +1
The practical importance of coherent forecasts in hierarchical forecasting has inspired many studies on forecast reconciliation. Under this approach, so-called base forecasts are p…
Exploring the representativeness of the M5 competition data
Evangelos Theodorou, Shengjie Wang, Yanfei Kang +3
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 i…
Exploring the social influence of Kaggle virtual community on the M5 competition
Xixi Li, Yun Bai, Yanfei Kang
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 pract…
Forecast with Forecasts: Diversity Matters
Yanfei Kang, Wei Cao, Fotios Petropoulos +1
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…
Improving the Accuracy of Global Forecasting Models using Time Series Data Augmentation
Kasun Bandara, Hansika Hewamalage, Yuan-Hao Liu +2
Forecasting models that are trained across sets of many time series, known as Global Forecasting Models (GFM), have shown recently promising results in forecasting competitions and…
Déjà vu: A data-centric forecasting approach through time series cross-similarity
Yanfei Kang, Evangelos Spiliotis, Fotios Petropoulos +3
Accurate forecasts are vital for supporting the decisions of modern companies. Forecasters typically select the most appropriate statistical model for each time series. However, st…