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Wouldn't your business be much more successful if you had a crystal ball that could tell you exactly what's happening next month, next year, or even more? With the latest technology from machine learning and data science, it's within our capacity—more than ever before—to learn about stable, repeating trends from the past and use them to make an accurate, sometimes exact, predicition of what the future holds.

Airline Bookings.


On the left is an example of monthly airline ticket bookings over an eight-year period. It’s clear there are several complex components to this historical data, including what appears to be a general growth in bookings over time as well as a common repeating pattern. Using machine learning, we can decompose the data into these components to better understand what will happen in the future.

The growth component captures natrual growth or decline over time. In this case study, we can see that the airline business has grown over the last eight years and will likely continue to do so going forward in 2016.

The cyclical component characterizes events that tend to repeat every so often, such as annual or semi-annual events. For the bookings data, this includes increases in revenue around holidays, the beginning of travel seasons, and more.

The stochastic component holds whatever is left over after removing the growth and cyclical information. Typically, the stochastic component carries information relating to news and other short-term events that have effects on booking revenue.