  Machine learning has been used in all kinds of fields. In this article, we
introduce how machine learning can be applied into time series problem.
Especially, we use the airline ticket prediction problem as our specific
problem. Airline companies use many different variables to determine the flight
ticket prices: indicator whether the travel is during the holidays, the number
of free seats in the plane etc. Some of the variables are observed, but some of
them are hidden. Based on the data over a 103 day period, we trained our
models, getting the best model - which is AdaBoost-Decision Tree
Classification. This algorithm has best performance over the observed 8 routes
which has 61.35$\%$ better performance than the random purchase strategy, and
relatively small variance over these routes. And we also considered the
situation that we cannot get too much historical datas for some routes (for
example the route is new and does not have historical data) or we do not want
to train historical data to predict to buy or wait quickly, in which problem,
we used HMM Sequence Classification based AdaBoost-Decision Tree Classification
to perform our prediction on 12 new routes. Finally, we got 31.71$\%$ better
performance than the random purchase strategy.
