Topic: Optimal Forecast Reconciliation

Speaker: Prof. Rob Hyndman

Host: Professor Fan Ying

Date: Nov 30, 14:30-16:30

Venue: A618, New Main Building

Abstract:

Time series can often be naturally disaggregated in a hierarchical or grouped structure. For example, a manufacturing company can disaggregate total demand for their products by country of sale, retail outlet, product type, package size, and so on. As a result, there can be millions of individual time series to forecast at the most disaggregated level, plus additional series to forecast at higher levels of aggregation.

A common constraint is that the disaggregated forecasts need to add up to the forecasts of the aggregated data. This is known as forecast “coherence”. When we turn incoherent forecasts into coherent forecasts, we call it “forecast reconciliation”. I will show that the optimal reconciliation method involves fitting a linear regression model where the design matrix has one column for each of the series at the most disaggregated level. But with huge numbers of time series, the model is impossible to estimate using standard regression algorithms. I will show how the hts package for R solves this problem.

Bio of the Speaker:

Rob Hyndman is a professor of Statistics in the Department of Econometrics and Business Statistics at Monash University, Australia. He is also Editor-in-Chief of the International Journal of Forecasting and a director of the International Institute of Forecasters. Rob is the author of over 150 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. 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.

School of Economics and Management