2019-09-16, 14:30–15:00, Assembly Room
How to automatically identify, and describe, interesting patterns in timeseries data, such as trends, change-points and periodic behaviour.
There has significant progress in recent years in developing new, exciting, flexible, predictive ML models. However! Considerable expertise is still required to choose appropriate features/models AND the output of a model, whilst accurate, can be difficult to understand! When dealing with lots of timeseries data it would be useful if we had a system to automatically group and describe similar timeseries and fit a state-of-the-art predictive model all at once...
In this talk I outline an elegant piece of work called the Automatic Statistician (based on Gaussian Processes) and how I implemented a simple Python API which can automatically detect interesting structure in timeseries data (trends, change-points, periodicity, etc) as well as providing a plain English description of the result.