25 October 2007

Mr Chancellor, Madam Deputy Vice-Chancellor, colleagues, guests, and especially graduates.

I would like to congratulate all of you who are graduating tonight.

It is a great achievement to have completed a university degree, andyoushould all feel very, very proud of your accomplishment. This is one of the six great milestones in your life: the others being birth, death, marriage, parenthood and the day you finally pay off your HECS debt.

For the families and friends of graduates, it is also a time to celebrate and bask in some reflected glory. You have nurtured, encouraged and supported these graduands to get to this point, and you have every right to feel very proud today, too. Every one of you has had to make some sacrifices in order to be here tonight. As Martin Luther King Jr said

Nothing worthwhile is gained without sacrifice.

If a university degree was easy, it would not be such a valuable achievement.

This is a time for you to reflect on the past and contemplate what the future holds. That’s where I come in: the future is my business. I am a forecaster-I build mathematical models to help predict the future, and I train other people to do the same.

You might not realise that forecasting has been part of academic discourse for several thousand years. In the days of the Babylonian empire more than 2500 years ago, the students in the court of Babylon were taught to forecast using sheep’s livers. When the king went to war, one of his advisers would carry a rotting sheep’s liver. If a forecast was required, such as whether the next battle would be won or lost, the liver carrier would investigate the distribution of maggots in the liver. Certain patterns were omens of victory. Other patterns were omens of defeat.

We don’t use sheep livers at Monash, although some of our software still has bugs. But we still do forecasting, and I’d like to do a spot of forecasting for you tonight. I have two predictions to make.

  1. Much of what you’ve learned will soon be irrelevant.
  2. You will face problems that no-one has ever solved.

I guess the maggots aren’t in your favour tonight.

Let me explain what I mean.

1. Most of the detail of what you’ve learned here at Monash will be irrelevant within a few years. You may be proud of your mastery of the theory in your discipline and the techniques required in applying it, but the reality is that such details will only be valuable to you in the short-term.

The modern world is a dynamic environment, always changing and evolving. Consequently, specific methods, techniques and even theories will become redundant and out-dated before long. Some research suggests that a young person starting off on their careers today will change their jobs between 12 and 25 times, and work in up to five industry sectors during their working lives. You will need to continually up-date, retrain and re-educate to play a long-term role in the world.

But this is good news, not bad. While you have learned some things at Monash, you have also been taught how to learn things. One of the most important things you have learned here is how to think through problems, find solutions, and explore ideas. These skills will stand you in good stead throughout your lives, even when the specific details of what you have learned cease to be relevant.

2. You will have to deal with much greater challenges than any generation that has gone before you. No other generation has had to face the consequences of climate change, the devastation of much of the natural environment, and the difficult task of maintaining peaceful cooperation in a world of shrinking resources and expanding populations. These are not just scientific problems, they are also business problems.

Wherever you go from here, and whatever you end up doing, you will inevitably have to make decisions with environmental and social consequences. Unfortunately, decisions are often made based on how much money will be made rather than how much good will be achieved.

It is a sad fact that over the last few decades there has been an increasing preoccupation with money. The worth of a person is measured by their income. The value of a company is assessed by its share price. Yet deep down you all know that the real values in life have nothing to do with money. As the American journalist Henry Mencken wrote

The chief value of money lies in the fact that one lives in a world in which it is overestimated.

You will need to think beyond your immediate training in accounting, finance and business, to address the daunting challenges facing our world in a way that recognizes social, environmental and moral values, as well as the financial situation.

This is an exciting time to be alive. Although our world is an uncertain and changing environment, it is also full of possibilities and there is a certain sense of adventure in the journey. There are two possible responses you can make to a changing world. First, you can be tossed to and fro wherever the waves of change take you. If you follow that course, you will be miserable, helpless and ineffective. Alternatively, you can embrace the changes that happen, ride the waves of uncertainty, and drive further changes yourself. Following that course will lead you to continue achieving, learning and improving. Most progress has come from people who were not content to accept the status quo, but were driven by a desire to improve things. Change is hard, but stagnation is fatal!

I hope you can look back on your experience of university recognizing the privilege of being here. Universities are wonderful places to be. I never intended becoming an academic, but I could never bear to leave. The 19th century British Poet Laureate, John Masefield, wrote

There are few earthly things more beautiful than a university … a place where those who hate ignorance may strive to know, where those who perceive truth may strive to make others see.”

I hope Monash has helped you grow to see the world more clearly and yourself more honestly.

Once again, I congratulate you and I wish you well for the future, whatever shape it takes.



1 2 3 5
March 7th, 2017

Coherent Probabilistic Forecasts for Hierarchical Time Series

February 28th, 2017

Forecasting with temporal hierarchies

February 14th, 2017

The Australian Macro Database: An online resource for macroeconomic research in Australia

February 14th, 2017

Macroeconomic forecasting for Australia using a large number of predictors

January 13th, 2017

Visualising forecasting algorithm performance using time series instance spaces

January 1st, 2017

Associations between outdoor fungal spores and childhood and adolescent asthma hospitalisations

January 1st, 2017

Grouped functional time series forecasting: an application to age-specific mortality rates

December 7th, 2016

Exploring the influence of short-term temperature patterns on temperature-related mortality: a case-study of Melbourne, Australia

October 13th, 2016

Reconciling forecasts: the hts and thief packages

September 20th, 2016

smoothAPC package for R

September 20th, 2016

stR package for R

September 15th, 2016

Forecasting large collections of related time series

August 22nd, 2016

thief package for R

June 21st, 2016

Exploring time series collections used for forecast evaluation

May 25th, 2016

ISCRR time series workshop

May 6th, 2016

Automatic foRecasting using R

February 29th, 2016

On sampling methods for costly multi-objective black-box optimization

February 19th, 2016

Dynamic Algorithm Selection for Pareto Optimal Set Approximation

February 4th, 2016

Forecasting uncertainty in electricity smart meter data by boosting additive quantile regression

January 30th, 2016

Bayesian rank selection in multivariate regressions

January 25th, 2016

Probabilistic Energy Forecasting: Global Energy Forecasting Competition 2014 and Beyond

January 24th, 2016

Long-term forecasts of age-specific participation rates with functional data models

January 1st, 2016

Bagging exponential smoothing methods using STL decomposition and Box-Cox transformation

January 1st, 2016

Fast computation of reconciled forecasts for hierarchical and grouped time series

December 31st, 2015

Measuring forecast accuracy

November 26th, 2015

Forecasting hierarchical and grouped time series through trace minimization

November 2nd, 2015

Forecasting big time series data using R

October 7th, 2015

Optimal forecast reconciliation for big time series data

October 5th, 2015

Google workshop: Forecasting and visualizing big time series data

September 16th, 2015


August 25th, 2015

New IJF editors

August 17th, 2015

Machine learning bootcamp

August 7th, 2015

Statistical issues with using herbarium data for the estimation of invasion lag-phases

June 30th, 2015

Exploring the feature space of large collections of time series

June 26th, 2015

Exploring the boundaries of predictability: what can we forecast, and when should we give up?

June 25th, 2015

Automatic algorithms for time series forecasting

June 23rd, 2015

MEFM: An R package for long-term probabilistic forecasting of electricity demand

June 19th, 2015

Probabilistic forecasting of peak electricity demand

June 10th, 2015

Do human rhinovirus infections and food allergy modify grass pollen–induced asthma hospital admissions in children?

June 8th, 2015

STR: A Seasonal-Trend Decomposition Procedure Based on Regression

June 4th, 2015

Probabilistic time series forecasting with boosted additive models: an application to smart meter data

June 1st, 2015

Large-scale unusual time series detection

May 26th, 2015

Visualization of big time series data

May 22nd, 2015

Probabilistic forecasting of long-term peak electricity demand

April 20th, 2015

A note on the validity of cross-validation for evaluating time series prediction

April 4th, 2015

Discussion of “High-dimensional autocovariance matrices and optimal linear prediction”

April 1st, 2015

Change to the IJF editors

February 23rd, 2015

Visualization and forecasting of big time series data

January 12th, 2015

Visualizing and forecasting big time series data

December 24th, 2014

Bivariate data with ridges: two-dimensional smoothing of mortality rates