Predicting Airfares using Machine Learning

Good research ideas can be turned into interesting and successful businesses. Case in point, a company called Farecast that provides an airfare search engine (similar to Orbitz). The website distinguishes itself by using machine learning techniques to predict the future price of airfares based on historical data. Thus it recommends whether a user should buy immediately (because the price is going up) or wait for a lower price (as the price is likely to fall).

I'm interested in the company in particular because it is based on technology developed by my thesis advisor Craig Knoblock (together with other researchers at USC and the University of Washington). The original idea was described in this paper "To buy or not to buy: Mining airline fare data to minimize ticket purchase price" by Oren Etzioni, Craig A. Knoblock, Rattapoom Tuchinda, and Alexander Yates. The paper was published at KDD back in 2003, but is still a very good read. - Needless to say, I am a big fan of Craig's research. He has a knack for discovering new research problems that are both interesting and practical (and sometimes even commercializable).

According to a post on TechCrunch, the company has just started predicting airfares on some international routes. This will be very useful for those of us not living in the US and who spend a significant portion of our incomes on airfares. - I might be able to travel home and see the family a bit more often.