Description: The purpose of this article is to provide a refresher on regression and times series models so that actuaries will feel more comfortable building forecasting models.

Description: A summary of a new feature in the newsletter: a forecasting application contest. The Forecasting and Futurism Section will award a 16GB iPad to the section member who develops the best actuarial application of a judgmental forecasting method, incorporating best practices outlined in this issue’s article titled “Best Practices in Judgmental Forecasting.” In addition, we will feature the winning application in an issue of the newsletter.

Description: At the 2009 SOA Annual Meeting in Boston, Nassim Nicholas Taleb shared some thoughts on Black Swans, operating in the fourth quadrant, and living in the world of Extremistan versus Mediocristan during his keynote speech, all of which can be found in his published work. The follow-up session, summarized in this article, addresses the real question: What do we actuaries do about these phenomena?

Description: Neural networks have fallen only to rise again twice. The latest incarnation borrows much from the past but adds elements to support deep learning. This article demonstrates the state of the art in deep neural network programming.

Description: The first annual SOA Predictive Analytics Symposium was held in Chicago and exceeded all expectations in attendance, presentation quality, and relevant content.

Description: Describes what a Delphi study is and why actuaries might want to use one.

Description: A discussion on the starting point of building a predictive model.

Description: A summary of how the Forecasting and Futurism Section is networking with other SOA sections and with external organizations to collaborate on various projects. Also highlights new events and projects happening on the forecasting and futurism front.

Description: The author discusses the use of genetic algorithms as they apply to actuaries, specifically the application of characteristics and their potential to solve life insurance asset and liability management problems. He also highlights various ways genetic algorithms can be used to solve a variety of actuarial problems.

Description: An introduction to Hidden Markov Models, which are used extensively in speech and facial recognition technology. They are a useful tool in forecasting time series and in developing categorizations of data based on the values of the data themselves.