Thinking big at Yahoo


23 April 2015

data science
time series

I’m speaking in the “Yahoo Labs Big Thinkers” series on Friday 26 June. I hope I can live up to the title!

My talk is on “Exploring the boundaries of predictability: what can we forecast, and when should we give up?”  Essentially I will start with some of the ideas in this post, and then discuss the features of hard-to-forecast time series.

So if you’re in the San Francisco Bay area, please come along. Otherwise, it will be streamed live on the Yahoo Labs website.


Why is it that we can accurately forecast a solar eclipse in 1000 years time, but we have no idea whether Yahoo’s stock price will rise or fall tomorrow? Or why can we forecast electricity consumption next week with remarkable precision, but we cannot forecast exchange rate fluctuations in the next hour?

In this talk, I will discuss the conditions we need for predictability, how to measure the uncertainty of predictions, and the consequences of thinking we can predict something more accurately than we can.

I will draw on my experiences in forecasting Australia’s health budget for the next few years, in developing forecasting models for peak electricity demand in 20 years time, and in identifying unpredictable activity on Yahoo’s mail servers.