Keynote address, International Symposium on Forecasting, June 2009.
Extremely bad data, extremely poor methods and extremely difficult problems will be used as the basis of some extremely useful lessons. I will describe three cases from my consulting experience and draw some general lessons that are widely applicable.
The first case involved forecasting passenger traffic on an Australian airline. The data showed variations due to school holidays, major sporting events, competitor activity, industrial disputes, changes in fare structures, and other factors. I will discuss the types of models that can be used to successfully forecast such data.
The Australian government subsidizes some pharmaceutical products, and requires forecasts of the likely expenditure on such products. After two consecutive years in which the expenditure was under-estimated by half a billion dollars, I was asked to review their forecasting procedures and recommend how to do it better. I will describe the results.
My third example involves forecasting the maximum electricity demand in any half hour period up to ten years in advance using only ten years of historical data. This seemingly impossible task was resolved so successfully, that the methods developed are now used as the basis of official forecasts for three states of Australia.
These three diverse examples will be used to draw some general conclusions about model complexity, structural change and forecast uncertainty.