17 citations · 18 across the 3 of their papers we have counts for
7 papers
DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting
Wei Fan, Shun Zheng, Xiaohan Yi +4
Periodic time series (PTS) forecasting plays a crucial role in a variety of industries to foster critical tasks, such as early warning, pre-planning, resource scheduling, etc. Howe…
Impact of pandemic fatigue on the spread of COVID-19: a mathematical modelling study
Disheng Tang, Wei Cao, Jiang Bian +4
In late-2020, many countries around the world faced another surge in number of confirmed cases of COVID-19, including United Kingdom, Canada, Brazil, United States, etc., which res…
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…
MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler
Zhining Liu, Pengfei Wei, Jing Jiang +3
Imbalanced learning (IL), i.e., learning unbiased models from class-imbalanced data, is a challenging problem. Typical IL methods including resampling and reweighting were designed…
Trimming the Sail: A Second-order Learning Paradigm for Stock Prediction
Chi Chen, Li Zhao, Wei Cao +2
Nowadays, machine learning methods have been widely used in stock prediction. Traditional approaches assume an identical data distribution, under which a learned model on the train…
Self-paced Ensemble for Highly Imbalanced Massive Data Classification
Zhining Liu, Wei Cao, Zhifeng Gao +4
Many real-world applications reveal difficulties in learning classifiers from imbalanced data. The rising big data era has been witnessing more classification tasks with large-scal…