第十届“中国人民大学国际统计论坛”（RUC IFS 2025）将于2025年7月4-6日在中国人民大学召开。大会邀请美国、澳大利亚等国家和地区的知名学者参会，将就“统计学发展史、数理统计、数据科学与人工智能、生物统计学前沿探究、政府统计、金融统计”等问题展开深入交流与讨论。

本次介绍主题报告人Rob J Hyndman，报告主题为“Improving forecasts via subspace projections”。

Rob J Hyndman

Title

Improving forecasts via subspace projections

Abstract

Univariate, multivariate, and hierarchical forecasts can all be improved using projections onto linear subspaces, regardless of what forecasting method is used. I will show some theoretical guarantees of this statement, and demonstrate using empirical applications how linear projections can lead to (sometimes dramatic) improvements in forecast accuracy.

The procedure involves creating new time series that are linear combinations of the observed time series. These are then forecast, along with the original series, and all forecasts are adjusted via a projection matrix to ensure they satisfy the linear constraints.

This procedure is now widely used for hierarchical forecasting. It has the potential to revolutionise multivariate forecasting as well.

Biography

Rob J HyndmanFAA FASSA is a Professor of Statistics in the Department of Econometrics and Business Statistics at Monash University, and an elected Fellow of both the Australian Academy of Science and the Academy of Social Sciences in Australia. He is the author of over 200 research papers and 5 books in statistical science. In 2007, he received the Moran medal from the Australian Academy of Science for his contributions to statistical research, especially in the area of statistical forecasting. In 2021, he received the Pitman medal from the Statistical Society of Australia. For over 30 years, Rob has maintained an active consulting practice, assisting hundreds of companies and organizations around the world. He has won awards for his research, teaching, consulting and graduate supervision.