Improve Machine Learning by Model Averaging


Speaker: Professor Zhang Xinyu

Topic: Improve Machine Learning by Model Averaging

Date: September 18

Time: 17:00 pm

Venue: Room 1506, Building 9

Sponsor: School of Mathematics and Statistics, Institute of Science and Technology


This paper introduces novel methods to combine forecasts made by machine learning techniques. Machine learning methods have found many successful applications in predicting the response variable. However, they ignore model uncertainty when the relationship between the response variable and the predictors is nonlinear. To further improve the forecasting performance, we propose a general framework to combine multiple forecasts from machine learning techniques. Simulation studies show that the proposed machine-learning-based forecast combinations work well. In empirical applications to forecast key macroeconomic and financial variables, we find that the proposed methods can produce more accurate forecasts than individual machine learning techniques and the simple average method.